analysis of human brain mri - diva...
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ACTAUNIVERSITATIS
UPSALIENSISUPPSALA
2014
Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Medicine 1000
Analysis of Human Brain MRI
Contributions to Regional Volume Studies
RICHARD NORDENSKJÖLD
ISSN 1651-6206ISBN 978-91-554-8957-1urn:nbn:se:uu:diva-222376
Dissertation presented at Uppsala University to be publicly examined in Gunnesalen, Entrance10, Uppsala University Hospital, Uppsala, Tuesday, 10 June 2014 at 09:15 for the degree ofDoctor of Philosophy (Faculty of Medicine). The examination will be conducted in Swedish.Faculty examiner: Göran Starck (Sahlgrenska University Hospital).
AbstractNordenskjöld, R. 2014. Analysis of Human Brain MRI. Contributions to Regional VolumeStudies. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty ofMedicine 1000. 73 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-8957-1.
Many disorders are associated with regional brain volumes. The analysis of these volumesfrom MR images often requires sequential processing steps such as localization and delineation.It is common to perform volumetric normalization using intracranial volume (ICV, the totalvolume inside the cranial cavity) when comparing regional brain volumes, since head size variesconsiderably between individuals. Multiple methods for estimating ICV and procedures forvolume normalization exist.
A method for interhemispheric surface localization and extraction, using both intensityand symmetry information and without time consuming pre-processing, was developed.Evaluations of hemisphere division accuracy as well as suitability as a pre-processing stepfor interhemispheric structure localization were made. The performance of the method wascomparable to that of methods focusing on either of these tasks, making it suited for use in manydifferent studies.
Automated ICV estimations from Freesurfer and SPM were evaluated using 399 referencesegmentations. Both methods overestimated ICV and estimations using Freesurfer containederrors associated with skull-size. Estimations from SPM contained errors associated with genderand atrophy. An experiment showed that the choice of method can affect study results.
Manual ICV estimation is very time consuming, but can be performed using only a subsetof voxels in an image to increase speed and decrease manual labor. Segmenting every nth sliceand stereology were evaluated in terms of required manual labor and estimation error, using thepreviously created ICV references. An illustration showing how much manual labor is requiredfor a given estimation error using different combinations of n and stereology grid spacing waspresented.
Finally, different procedures for ICV normalization of regional brain volumes wheninvestigating gender related volume differences were theoretically explained and evaluatedusing both simulated and real data. Resulting volume differences were seen to depend on theprocedure used. A suggested workflow for procedure selection was presented.
Methodological contributions that can aid the analysis of the human brain have beenpresented. The performed studies also contribute to the understanding of importantmethodological considerations for regional brain volume analysis.
Keywords: MRI, Brain, Volume, Hemisphere, Intracranial volume, Image analysis
Richard Nordenskjöld, Department of Radiology, Oncology and Radiation Science,Radiology, Akademiska sjukhuset, Uppsala University, SE-751 85 Uppsala, Sweden.
© Richard Nordenskjöld 2014
ISSN 1651-6206ISBN 978-91-554-8957-1urn:nbn:se:uu:diva-222376 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-222376)
If the brain was simple enough to be understoodwe would be too simple to understand it!
– Marvin Minsky
List of papers
This thesis is based on the following papers, which are referred to in the textby their Roman numerals.
I Nordenskjöld, R., Larsson, E.-M., Ahlström, H., Johansson, L.,Kullberg, J., Automated interhemispheric surface extraction inT1-weighted MRI using intensity and symmetry information, Journalof Neuroscience Methods (222c), 2014, pp. 97–105.
II Nordenskjöld, R., Malmberg, F., Larsson, E.-M., Simmons, A.,Brooks, S. J., Lind, L., Ahlström, H., Johansson, L., Kullberg, J.,Intracranial volume estimated with commonly used methods couldintroduce bias in studies including brain volume measurements,NeuroImage (83C), 2013, pp. 355–360.
III Nordenskjöld, R., Malmberg, F., Larsson, E.-M., Ahlström, H.,Johansson, L., Kullberg, J., Manual intracranial volume estimationfrom MRI: labor reduction and estimation error, Submitted.
IV Nordenskjöld, R., Malmberg, F., Larsson, E.-M., Simmons, A,Ahlström, H., Johansson, L., Kullberg, J., Intracranial volumenormalization methods: considerations when investigating genderdifferences in regional brain volume, Submitted.
The inclusion of the accepted articles in this thesis has been made in com-plience with the publishers author rights.
Related Work
The author has also contributed to the following work:
1. Benedict, C., Brooks, S. J., Kullberg, J., Burgos, J., Kempton, M. J.,Nordenskjöld, R., Nylander, R., Kilander, L., Craft, S., Larsson, E.-M., Johansson, L., Ahlström, H., Lind, L., Schiöth, H. B., ImpairedInsulin Sensitivity as Indexed by the HOMA Score Is Associated WithDeficits in Verbal Fluency and Temporal Lobe Gray Matter Volume inthe Elderly, Diabetes Care (35:3), 2012, pp. 488–494.
2. Brooks, S. J., Benedict, C., Burgos, J., Kempton, M. J., Kullberg, J.,Nordenskjöld, R., Kilander, L., Nylander, R., Larsson, E.-M., Johans-son, L., Ahlström, H., Lind, L., Schiöth, H. B., Late-life obesity is asso-ciated with smaller global and regional gray matter volumes : a voxel-based morphometric study, International Journal of Obesity (37:2), 2012,pp. 230–236.
3. Malmberg, F., Strand, R., Kullberg, J., Nordenskjöld, R., Bengtsson,E., Smart Paint - A New Interactive Segmentation Method Applied toMR Prostate Segmentation, Prostate MR Image Segmentation GrandChallenge (PROMISE’12), a MICCAI 2012 workshop, 2012.
4. Malmberg, F., Strand, R., Nordenskjöld, R., Kullberg, J., Seeded Seg-mentation Based on Object Homogeneity, Proceedings of the 21st Inter-national Conference on Pattern Recognition (ICPR), 2012.
5. Velickaite, V., Cavallin, L., Kullberg, J., Nordenskjöld, R., Simmons,A., Lind, L., Ahlström, H., Wahlund, L., Larsson, E., Comparison ofvisual assessment of medial temporal lobe atrophy (MTA) on MRI, au-tomatic hippocampal volume measurement and cognitive performancein a 75-year-old population, Proceedings of the 29th Annual ScientificMeeting of the European Society for Magnetic Resonance in Medicineand Biology (ESMRMB), 2012.
6. Benedict, C., Brooks, S. J., Kullberg, J., Nordenskjöld, R., Burgos, J.,Le Grevès, M., Kilander, L., Larsson, E.-M., Johansson, L., Ahlström,H., Lind, L., Schiöth, H. B., Association between physical activity andbrain health in older adults, Neurobiology of Aging (34:1), 2013, pp.83–90.
7. Westman, E., Cavallin, L., Simmons, A., Spulber, G., Kullberg, J., Nor-denskjöld, R., Velickaite, V., Minthon, L., Eriksdotter, M., Aarsland,D., Winblad, B., van Westen ,D., Abdulilah Mohammed, A., Beyer, M.,Muller, S., Wallin, A., Larsson, E.-M., Wahlund, L.-O., Imaging Cog-nitive Impairment Network (ICINET), Proceedings of the 11th Interna-tional Conference On Alzheimer’s & Parkinsons’s Diseases (AD/PD),2013.
8. Malmberg, F., Nordenskjöld, R., Strand, R., Kullberg, J., SmartPaint -En mjukvara för interaktiv segmentering av medicinska bilder, Proceed-ings of the 15th Conference of Röntgenveckan, 2013.
9. Velickaite, V., Nordenskjöld, R., Kullberg, J., Kilander, L., Westman,E., Simmons, A., Cavallin, L., Lind, L., Ahlström, H., Wahlund, L.-0., Larsson, E.-M., Qualitative and quantitative MR analysis of medialtemporal lobe atrophy and posterior atrophy in a 75–year-old popula-tion, Proceedings of the 30th Annual Scientific Meeting of the EuropeanSociety for Magnetic Resonance in Medicine and Biology (ESMRMB),2013.
10. Malmberg, F., Strand, R., Nordenskjöld, R., Kullberg, J., An Interac-tive Tool for Deformable Registration of Volume Images, Proceedingsof Svenska Sällskapet för Automatiserad Bildanalys (SSBA), 2014.
11. Malmberg, F., Nordenskjöld, R., Strand, R., Kullberg, J., SmartPaint -A Tool for Interactive Segmentation of Medical Volume Images, Manuscript.
12. Voevodskaya, O., Simmons, A., Nordenskjöld, R., Kullberg, J., Ahlström,H., Wahlund, L.-O., Larsson, E.-M., Westman, E., Intracranial volumeadjustment of MRI-derived regional brain volumes in large scale cross-sectional volumetric studies, Manuscript.
Contents
Summary in Swedish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.2 Overall aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.3 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Digital images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.1 Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2 Imaging techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 The human brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.1 Anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.2 Why volume matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Image analysis of the human brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.1 Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2 Delineation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.3 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5 Midsagittal region in image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.2 Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.3 Spatial normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.4 Estimation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.5 Corpus callosum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6 Intracranial volume in image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426.2 Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436.3 Volume normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446.4 Estimation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
7 Summary of papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527.1 Paper I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527.2 Paper II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557.3 Paper III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577.4 Paper IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618.1 Past . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618.2 Present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628.3 Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658.4 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Abbreviations
2D two dimesional3D three dimesionalAC anterior commissureASF atlas scaling factorCC corpus callosumCSF cerebrospinal fluideTIV estimated total intracranial volumeGC Graph CutGM gray matterH hydrogenICA intracranial areaICV intracranial volumeIFT Image Foresting TransformMRI magnetic resonance imagingMR magnetic resonanceMSP midsagittal planePC posterior commissurePD-w proton density weightedPET positron emission tomographySPM Statistical Paramatric MappingT1-w T1 weightedT2-w T2 weightedTBV total brain volumeTE echo timeTIV total intracranial volumeTPM tissue probability mapTR repetition timeVBM voxel-based morphometryWM white matter
Summary in Swedish
Möjligheten att digitalt avbilda människokroppens interna strukturer har banatväg för datorstödd analys av hjärnans form, funktion och volymetriska egen-skaper utan invasiva åtgärder. Volymer hos olika hjärnstrukturer är viktigaatt analysera då de funnits associerade med olika sjukdomar, t.ex. hippocam-pusvolym och Alzheimers sjukdom.
Typiska steg för volymsestimering från bilder är att först lokalisera en struk-tur för att sedan avgränsa denna så noga som möjligt.
Lokalisering av ett område kan ske genom att transformera en bild till ettkänt koordinatsystem, t.ex. Talairach koordinater. Det koordinatsystemet byg-ger på att strukturer befinner sig vid en särskild position relativt andra kändapositioner. Systemet definieras av det plan som delar hjärnans hemisfärer,komissurer som kopplar ihop hemisfärerna, samt hjärnans dimensioner. Om-rådet som avgränsar hemisfärerna är därför viktigt att hitta för att kunna utföradenna transformation.
Området som avgränsar hemisfärerna är viktigt även för andra ändamål.Det är till exempel viktigt för att volymsbestämma hemisfärerna och för attlokalisera strukturer som befinner sig i området.
Då en strukturvolym bestämts är det ofta av intresse att jämföra den mellangrupper av individer för att hitta associationer. Det kan till exempel vara friskajämfört med sjuka eller jämförelser mellan kön. Då det är naturligt för män-niskor med större huvuden att även ha större strukturvolymer måste en kom-pensation för detta ske. Till det ändamålet används ofta intrakraniell volym(ICV) som är ett mått på den totala volymen innanför kraniet, vilket ocksåanses vara ett mått på en individs maximala hjärnvolym under sin livstid. Detfinns flera olika sätt att estimera denna volym och även flera sätt att kom-pensera för den.
Medicinsk bildanalys har avslöjat många associationer mellan strukturvolym,sjukdomar och levnadssätt. Då analysmetoderna som används förbättras kanmindre tydliga förändringar upptäckas och vår förståelse för den mänskligahjärnan öka.
Delarbete II detta arbete presenteras en metod som estimerar den yta som delar hemisfär-erna och samtidigt fokuserar på att ytan ska passera de strukturer som ligger
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mellan hemisfärerna. Existerande metoder som används till att finna struk-turer mellan hemisfärerna delar ofta dessa med ett plan vars placering baseraspå symmetri-information. Då regionen mellan hemisfärerna oftast är kurvadbör dessa metoder inte användas för noggrann delning. De metoder som nog-grant delar hemisfärerna utnyttjar vanligtvis intensitetsinformation för att hittaen avgränsing som följer regionen mellan hemisfärerna. De är ofta tidskrä-vande och kan reducera bort viktiga strukturer under förbehandling av bilden.Genom att kombinera symmetri och intensitetsinformation kunde en yta somutnyttjar fördelarna hos båda metodtyper beräknas.
Delarbete IIArbetet jämför två vanliga metoder för ICV estimering. 399 manuellt seg-menterade referenser användes för att undersöka estimeringsfel hos Freesurferoch Statistical Parametric Mapping (SPM). Båda metoderna överestimeradeICV och Freesurfers estimeringsfel var associerat med skallstorlek, medanSPMs estimeringsfel var associerat med kön och atrofi. Dessa associationerkan leda till missvisande studieresultat. Ett enkelt exempel visade även attstudieresultat kan påverkas av metodvalet för ICV estimering.
Delarbete IIIDet här arbetet gick ut på att utvärdera två tillvägagångssätt för att reducera ar-betsbördan vid manuell ICV estimering. Det första estimerar ICV genom seg-mentering av endast var n:te snitt. Det andra, ofta kallat stereologi, estimerarICV genom att placera ut punkter i var n:te snitt och beräkna antalet punk-ter som hamnade inom det intrakraniella området. Dessa två tillvägagångssättutvärderades med hjälp av 400 referenssegmenteringar med avseende på es-timeringsfel och arbetsbörda. En illustration av dessa förhållanden presenter-ades och kan användas som riktlinje vid manuell ICV estimering.
Delarbete IVI det sista arbetet jämförs olika metoder för att normalisera en strukturs volymmed hjälp av ICV då man studerar könsberoende volymsskillnader. En teo-retisk bakgrund för varje metod, samt presentation av en ny metod, följdesav experiment med simulerad och riktig data. Resulterande könsskillnader ivolym, vid experiment med både de simulerade och riktiga värdena, varier-ade beroende på vilken normaliseringsmetod som användes. Slutsatsen fråndetta arbete resulterade i ett flödesdiagram för hur man bör välja kompensa-tionsmetod vid analys av könsberoende volymsskillnader. Delar av arbetet ärapplicerbara vid analys av volymsskillnader mellan andra grupper än kön.
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SammanfattningEn avgörande faktor för kvalitén på studier innehållande regionala hjärnvoly-mer är hur bra estimeringarna av dessa är. En viktig del av estimeringen ärlokalisering av regionen av intresse för att man sedan ska kunna avgränsa den.Då volymer vanligtvis kompenseras för ICV resulterar det i att värden baser-ade på förhållandet mellan hjärnvolym och ICV används vid analys, vilket görestimeringen av ICV lika viktig. En kunskap om hur en kompensering för ICVkan ske på bästa sätt är ett krav för pålitliga studieresultat. Denna avhandlingsamt utförda delarbeten ger förhoppningsvis ett väl mottaget bidrag till förbät-tringar och kunskapsökning inom samtliga av dessa aspekter, vilket kommerleda till bättre framtida studier där regionala hjärnvolymer analyseras.
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1. Introduction
1.1 BackgroundThe ability to digitally image the internal organs of the human body has openedthe door to computer aided analysis of shape, function, and volumetric prop-erties of structures while still inside the body. The visualization of internalorgans has also added greatly to the understanding of physiology, pathologyand treatment response. With the increased possibilities introduced with med-ical imaging, the need for accurate and precise image analysis procedures hasarisen.
Development of computer aided and automated brain image analysis meth-ods has been ongoing for decades without slowing down. As the computa-tional power and image quality increases, more advanced algorithms can beused and methods with increased accuracy and/or performance can be devel-oped.
In order to estimate a regional volume from a medical image, the regionfirst needs to be located and then delineated in some way. It is sometimesnecessary to apply additional processing steps to an image in order to get agood estimation. Many studies developing methods to solve a specific taskhave been performed, and automated analysis pipelines have been created bycombining methods and applying them in a sequential order to an image. Eachstep in a sequential pipeline relies on the preceding step to function correctly.If for instance a localization step functions incorrectly, a volume might beestimated for the wrong region.
The analysis does not stop with a volume being estimated. In order tocompare regional brain volumes extracted from subjects having differentlysized heads, something else is needed. A common measure used to normalizeregional volumes is the intracranial volume (ICV) because it has been seenclosely related to the life-maximum brain size. This means in practice that forevery subject, both regional volume and ICV needs to be estimated, increas-ing the risk of estimation error affecting study results. A challenge that hasto be accepted is how regional volume should be normalized by ICV. Multi-ple procedures having different aims are commonly used, making it harder tocompare results from different studies.
Image based analysis has revealed many associations between regional brainvolume, diseases, and lifestyle. As analysis methods evolve, more detailed an-alyzes can be made to further increase our knowledge of the human brain.
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1.2 Overall aimThe overall aim of this thesis is to give an overview of the methodology com-monly used in image based volume analysis of the human brain, to increasethe knowledge of the analysis procedure, evaluate existing methodology, andto contribute with methodological improvements in this field. Study specificaims are given in the corresponding paper section in Chapter 7.
1.3 Structure of the thesisThis thesis is based on four papers. Paper I introduces a method for automati-cally locating the midsagittal region and estimating it using a surface. Paper IIinvestigates the possible bias introduced in studies when using two commonlyused methods to estimate ICV. Paper III investigates the relation betweenICV estimation error and the amount of manual labor required when estimat-ing ICV using only a subset of the image data. Finally, Paper IV investigateshow well and when different methods for ICV normalization are suited wheninvestigating gender related differences in regional brain volume.
The purpose of the framing text is to give insight into methods and proce-dures used in image based analysis of the human brain enabling the reader tobetter understand, and hopefully even enjoy, the conducted studies describedin the papers.
Chapter 2 gives a basic understanding of how images are represented in adigital environment and how they are created using different imaging tech-niques.
An introduction to the human brain is given in Chapter 3. It is not a compre-hensive atlas of human anatomy but gives an overview of the brains differenttissues and structures, and the importance of analyzing their volume.
Chapter 4 gives an overview of methods used in image analysis of the hu-man brain. As all papers are based on magnetic resonance (MR) data, themethods described are those applicable to images produced by this imagingtechnique.
Chapter 5 describes a special place in the brain called the midsagittal region.A definition of the region is followed by sections describing its importance,how it is localized, and how it is estimated.
Chapter 6 is dedicated entirely to ICV. The sections in this chapter describehow the volume is defined, used, and estimated.
A more detailed description of each individual paper is given in Chapter 7.Concluding the thesis is Chapter 8 providing a discussion of past, present,
and future work concerning regional brain volume analysis.
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2.2 Imaging techniquesPhotons are involved in many common imaging techniques (Fig. 2.2). Thelargest difference between these techniques is what wavelength (λ ), energylevel and emission source the imaged photons have. Photographic imagingregisters the amount of reflected or emitted photons in the visible light spec-trum. X-ray imaging is used to measure the amount of high energy photonspassing through an object. Both positron emission tomography (PET) andMRI use photons to image signals coming from inside the body. Below is anoverview of these four imaging techniques.
Photographic imagingIn photographic imaging (Fig. 2.2 a), photons originate from for example thesun or a light bulb. Depending on an objects opacity, photons either passthrough or are reflected on an objects surface. The change in λ distributionduring this process is what gives the object its specific color. By registeringthe photons that have interacted with the object, an image of it can be created.A 2D sensor containing cells registers the photons that hit each cell and the2D spatial location is directly determined by the cells position on the rectan-gular image sensor. Color images can be created by determining the photonwavelength distribution in each cell.
X-ray imagingX-ray imaging (Fig. 2.2 b) is similar to photographic imaging. A photon emit-ter is used to provide photons with desired wavelength. These photons havebetter penetration abilities compared to visible light, making it possible forthem to pass through objects that visible light can not. Photons with thesewavelengths are unfortunately also harmful to humans. As photons are trans-mitted through the object, a sensor on the opposite side of the object registersthe amount of photons that have passed through. Depending on the interiorstructure of the object, different amounts of photons will pass through it andhit the sensor.
PET imagingPET images (Fig. 2.2 c) are created by inserting short lived radioactive iso-topes into the body. These isotopes are connected to some molecule to mea-sure the molecules current position in the body. During the isotope decay, apositron is emitted. Eventually the positron will interact with an electron, cre-ating a pair of photons moving in approximately opposite directions. Becauseof this property it is possible to image the amount of decay, and therefore theamount of molecule uptake, at each location in the image. A detector ring, inwhich the object is placed, registers the location where the photon pair is de-tected. The origin of the decay is located in the proximity of the line betweenthe two points on the ring that detected the photons.
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the atomic nuclei one wants to resonate, a resonance frequency (called the Lar-mour frequency) can be calculated. Transmitting photons with this frequencywill cause the nuclei to absorb the photons energy and become exited. Afterabsorption the nuclei start to relax, i.e. they release their stored energy andstart to return to their original state. The relaxation rate is dependent on thenuclei’s surroundings, making different tissue characteristics separable. Asthe nuclei relax, changes occur in the magnetic field. This results in a sig-nal on which an image is based. Three magnetic field gradients are used tospatially encode the signal, enabling an image of the object to be created bytransforming the signal using a Fourier transform.
Different contrast between tissues can be obtained by creating differentlyweighted images. The parameters used to do so is the repetition time (TR)which is the time between photon transmissions, and the echo time (TE) whichis the time between a transmission and the data collection. Illustrated inFig. 2.3 are examples of differently weighted images. Short TE and TR givesa T1 weighted (T1-w) image, short TE and long TR gives a proton densityweighted (PD-w) image, and long TE and TR gives a T2 weighted (T2-w)image.
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3. The human brain
In this chapter an overview of human brain anatomy is given, along with whyregional brain volumes are important to study.
3.1 AnatomyAn overview of the human brain is given in Fig. 3.1. The three views shownare common in neurological MR images, and the anatomy is simplified forclarity.
Gray matterWhite matterCerebrospinal fluid
SkullNon-brain tissueMidsagittal region
Back of head to noseRight to left earFeet to scalp
a) b) c)
Figure 3.1. Simplified anatomy of the human brain as seen from three different views.a) Axial. b) Sagittal. c) Coronal.
The brain is roughly laterally symmetric (Fig. 3.1 a) meaning it has twohalves, or hemispheres, containing the same structures which are similar inshape and size. Surrounding the brain tissue is the skull, or cranium as thispart of the skull is called. Inside the skull, in the intracranial cavity, the brainrests in a protecting layer of cerebrospinal fluid (CSF).
Brain tissueThe brain tissue can be divided into gray matter (GM) and white matter (WM).Depending on the tissue type and spatial location, a specific brain function isassociated with that region. GM is the tissue that does the actual "thinking"
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and information processing. It can be divided into cortex surrounding thebrain, and deep nuclei lying embedded in WM. WM acts as a connector be-tween regions of GM, so that communication between different brain regionscan occur (Sonesson and Sonesson, 2001, p. 139). The CSF acts as a shockabsorbent to prevent the brain from taking damage from outer forces, as amedium preventing the brain from collapsing under its own weight, and as anagent for nutrition transportation across the central nerve system (Sonessonand Sonesson, 2001, p. 172–173).
Midsagittal regionThe region between the hemispheres is a special place. This region is referredto as the midsagittal region (Fig. 3.2) and contains "one of a kind" structures.It also contains all WM connections between the hemispheres, known as com-missures. A more detailed description of this midsagittal region is given inChapter 5.
Corpus callosumBrain stemSeptum pellucidumFornixAnterior commissure
Posterior commissureThird ventricleCerebral aqueductFourth ventricleInterhemispheric fissure
Figure 3.2. Simplified anatomy of the human brain midsagittal region.
3.2 Why volume mattersVolume analysis is important for associating diseases, disorders and lifestyleto regional volume changes in the brain. Since specific regions are linked todifferent brain functions, finding associations between a regional volume anda disease can aid in future diagnostics or treatment. It is also important to
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study how different lifestyles affect brain volume as it can increase the under-standing of how to better nurture a healthy brain.
Hippocampus, a deep GM nucleus, is involved in the process of memorycreation. In a study by Scoville and Milner (1957) a patient had surgicaldestruction of both hippocampi, and was thereafter unable to form any newmemories. Even differences in hippocampal volume have been seen associ-ated with cognition and Alzheimer’s disease (Schuff et al., 2009).
A review by Oertel-Knöchel and Linden (2011) lists multiple studies con-cluding individuals suffering from Schizophrenia to have smaller volume incorpus callosum (CC), a WM commissure (Fig. 3.2) connecting the hemi-spheres, than controls.
Associations between physical activity and brain volume in normal elderlysubjects have also been found by for example Benedict et al. (2013).
Differences between genders are also important to study. It has been sug-gested that normal age related atrophic rates are different for males and fe-males (Blatter et al., 1995). This information is critical, since a proper diag-nosis based on regional brain volumes can not be performed without knowingwhat is normal for an individual under consideration.
To find associations between regional brain volume and diseases, disorders,or lifestyle, multiple subjects can be analyzed cross-sectionally, i.e. all datais collected at one time point for each studied subject. From a cross-sectionalstudy it is however not possible to determine causality, i.e. it can be the diseasethat causes changes in volume, or a change in volume that causes a disease.To study causality a randomized intervention study is needed, but longitudinaldata collected over time for each studied subject can give a good indication.
There is still much to learn about the human brain, and image analysis willbe needed for a long time still.
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prior information about the position of other regions relative the landmarks.This is a basic form of spatial normalization where an image is aligned withprior information to place regions "where they are supposed to be" (4.2 a). Thelandmarks can, in cases where a computer method is able, be placed withouthuman interaction. This enables hard to detect regions to be localized by usingother regions that are detected more easily.
AtlasA common form of prior knowledge is an atlas or a template. Before movingon, the difference between these two terms needs to be defined. In this thesisan atlas is referred to as a map of a brain indicating what is located in a specificlocation. A template is a form of atlas but is focused on shape and intensitypatterns in an image rather than anatomical labels. The terms can be usedinterchangeably in many cases and as a template is interpreted as a form ofatlas, the term atlas will be favored in this thesis unless otherwise is used bythe authors of a method being described.
When using an atlas as prior knowledge it is important that it is similar to thebrains being processed and has a known coordinate space. By using variousmethods to register an image with the atlas, the image can be transformed intothe known coordinate space of the atlas. Knowing the location of a regionin the atlas then automatically reveals the approximate location of the sameregion in the image. Below, two image registration procedures are described.
Affine registrationThe first method is called affine registration, described by for example Wol-berg (1994, pages 47–51). This is performed in a way such that, when animage is transformed into another coordinate space, parallel lines in an imageremain parallel and straight even after the registration. An affine transformused to register the image consists of a combination of translation, skewing,scaling, and rotation. The same transformation is applied to all voxels in theimage, changing its location as shown in Fig. 4.2 b). An example of whenan affine registration is appropriate is when transforming an image into theTalairach coordinate space (Talairach and Tournoux, 1988). The Talairachatlas space is defined based on brain dimensions and three orthogonal planes,making it possible for an image to be well registered with it using an affinetransform.
The affine transformation needed to register an image with an atlas can befound in different ways. One way is to use enough landmarks to define thetransformation. Another way is to use an optimization method that maximizesthe image-atlas correspondence, as done in the commonly used Freesurferanalysis pipeline (Fischl et al., 2002).
The limitation with affine registration is that it is not likely to produce exactvoxel correspondence between an image and an atlas. The transform is applied
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to the entire image, making it impossible to correctly register regions havingnon linear morphological differences between an image and the atlas.
Non-rigid registrationAnother type of registration is non-rigid registration. Here, a voxel in the im-age may be transformed using its own transformation and not a global one asin affine registration. This makes it possible to get a direct voxel-voxel cor-respondence (Fig. 4.2 c). The result is a map where each voxel has a vectorof how it is supposed to relocate to fit directly over the corresponding voxelin the atlas. One approach described by Ashburner and Friston (1999), whichis also applied in the widely used Statistical Paramatric Mapping (SPM) anal-ysis pipeline (Ashburner and Friston, 2005), is to use linear combinations ofapproximately 1000 cosine transform bases. Each base is scaled using an opti-mization method so that the difference between image, after being transformedusing the base combination, and atlas is minimized. This method assumes theimage is pre-aligned with the atlas using an affine transformation. Typicallyan optimization method benefits from having a starting guess close to the realsolution in order to better converge to a good registration.
4.2 DelineationOnce a region of interest has been localized, it needs to be delineated in orderto estimate its volume. Humans are generally poor at doing this consistently,but computers can apply identical rules for constant delineation over all imagesprocessed.
If an image has been registered with voxel-voxel correspondence, the delin-eation is completed if the boundaries of a region is known in the atlas whosecoordinate space the image has been transformed to. If however the voxelsroughly correspond to a known location in the atlas, some further processingis required.
There are several approaches for delineation once a localization step hasbeen performed. Once a few reliable landmarks located inside a region havebeen selected, commonly referred to as seeds in delineation methods, theycan be expanded according to some set of rules. Below, a few common seedbased methods are described. Although pixel is used when referring to imageelements, voxel is an equally usable term in most cases.
Pixel basedRegion growing is an iterative expansion of seeds that either excludes or in-cludes their neighboring pixels according to some rule, commonly based onthe intensity value being inside a predefined threshold. If a neighbor is in-cluded, it becomes a new seed and its neighbors are checked for possible in-clusion. Once no more inclusions are made, one or more objects originating
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neighboring pixels are dark valleys and object contours are bright peaks. Byrelating seeds to water sources, the topological image is flooded with waterpouring from the seeds. Water travels downwards and floods all valleys as thewater level rises. As basins of water originating from different sources col-lide, a dam is put up to keep them separated. All seed pixels expand, fillingthe darkest pixels reachable by water before moving on to the more brighterones. When the whole image has been processed it will consist of dams de-lineating basins originating from each individual water source. Water sourcescan be assigned by either turning all regional minima to sources, i.e. a pixelwho is at the same height as the water level with no flooded neighbors, or byplacement of seeds at locations of interest. Using the former method can leadto an object being divided into multiple smaller objects if it contains more thanone local minimum, making further processing necessary. In the final trans-formed image, each seed will be surrounded by a closed contour located at thehighest region between it and the closest neighboring seeds. This is why thewatershed transform commonly is applied to images displaying object con-tours as high intensity regions (gradient images), making a regions boundarylocated along dams.
Graph basedA family of delineation methods is the graph based methods. Expressing animage as a graph enables the assignment of additional information. A graphconsists of nodes, each representing a pixel in the image, and edges linkingnodes together. An edge between two nodes is required for them to be consid-ered neighbors (Fig. 4.3). The edge can contain a value specifying a cost for amethod to use that edge for some purpose (Fig. 4.4 a). The cost can be basedon for instance distance or intensity difference between nodes. Two examplesof graph based methods are Image Foresting Transform (IFT) and max-flowmin-cut.
The IFT (Falcão et al., 2004) finds the shortest paths from seeds, placedboth in and outside an object, to other nodes. All paths start from one of theselected seeds and the path that is in turn for expansion is determined by a pathcost function. The path to expand is the one that produces the shortest path costwhen an additional neighboring node is included. When a node is included ina path, it is labeled according to the seed from which the path originated. Sincethere are no paths that can reach the same node while having a lower path cost,the labeling of each node is final and trees will form originating from eachseed. An object is defined as all nodes reachable from its seed using a shorterpath than required for any other seed. An important factor is that all costs forpath expansion need to be non-negative, as the assumption of a shortest pathreaching a node first becomes invalid otherwise. Another important factor ishow tie-breaking is performed. It is possible that multiple paths can include anadditional node producing new paths of equal length. Depending on how thisis handled, different results will be obtained. An example of this is illustrated
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Root node which can be seen as the trees originNormal nodeLeaf node has no edge between itself and a node further from the root
a) b)
Figure 4.3. Example of graph structures. a) A tree is a connected graph having nocycles, i.e. no node can be revisited if each edge is usable only once. If a part of thegraph becomes disconnected from the tree it is called a branch. b) Adding a few edgesto the tree results in another graph. It is still a connected graph, but not a tree as itcontains cycles.
in Fig. 4.4 b) where the top right node can be added to either path. Consideringa larger graph, and tie-breaking occurs early in a path, this could have a largeeffect on the result as the path continues.
Considering a network of pipes where each pipe has a specific maximal ca-pacity of flow, Max-flow methods determine the maximal flow from a sourceto a destination (sink) in this network. Expressed as a graph the network is thegraph itself, the edges represent pipes with their costs indicating the capacity,and the nodes represent pipe connections. In image analysis, this methodol-ogy can be used for structure delineation based on the equality between max-flow and min-cut (Ford and Fulkerson, 1956) solutions. A min-cut is the setof edges that, when removed, cuts all paths between the source and the sinkwhile having the minimal sum of edge weights (capacities). Ford and Fulker-son (1956) describes a method for obtaining max-flow. While there is a pathbetween source and sink where all edges have remaining capacity, send flowequal to the least remaining capacity of any edge on the path, remove the sat-urated edges, and add the flow to the maximum flow. When no paths remain,the graph will be disconnected and form subgraphs. The set of edges includedin the disconnection is the min-cut. By assigning structure and backgroundseeds as source and sink respectively, the min-cut consists of the edges thatdelineate the seeded structure (Fig. 4.4 c).
An efficient max-flow min-cut method proposed by Boykov and Kolmogorov(2004), referred to as Graph Cut (GC) for the remainder of this thesis, gen-erates a min-cut by iterating over three stages. Growth, augmentation, and
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adoption. Search trees with roots in source and sink respectively are expandedin the growth step. The expansion is performed until the two search trees con-nect. This is when the augmentation stage begins, and flow along the pathformed by the two search trees through the connection is added until at leastone edge becomes saturated and gets removed from the tree. This makes somebranches disconnected from their root and the adoption stage begins. Theadoption stage rebuilds the two trees so that no loose branches exist. Thisis done by appending the root node of the branch to a neighboring branch’sleaf node, or to simply remove it and include the branch during subsequentgrowing. After the adoption stage, the growth stage resumes. The iterationscontinue until there are no possible connections between the search trees.
a) Graph
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b) Image Foresting Transform1
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c) Max-flow min-cut
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Figure 4.4. Example of graph based delineation methods. a) Graph example withassigned edge costs and colored seeds. b) Image Foresting Transform step by steplabeling of shortest path in graph shown in a). The path cost is determined as thesum of all included edges. The yellow nodes labeling depends on the tie-breakingscheme used. Bold edges are included in a path. c) Max-flow min-cut based optimiza-tion method step by step execution. Based on the graph in a) with flow allowed eastand southward. Bold edges are updated, remaining capacity in each updated edge isshown, and min-cut is illustrated in red.
4.3 Pre-processingBefore the localization and/or delineation of a region, pre-processing steps canbe applied to simplify the task. These steps can require their own localizationand/or delineation, and are therefore described after the delineation section.
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Intensity normalizationTypical MR images have relative intensities. i.e. one kind of tissue does nothave the same intensity between images. Intensity non-uniformity can alsooccur due to for example inhomogeneities in B0 or interaction between theimaged object and the scanning device (Belaroussi et al., 2006). In order toanalyze an image based on intensity it is therefore common to perform in-tensity normalization. Depending on the analysis that follows, the intensitycan be normalized to different degrees. A simple procedure is to normalizethe intensity histogram making the intensities span a specified range. If intra-tissue intensities need to be near constant a bias field compensating for non-anatomically driven intensity variations can be estimated, as in for exampleSPM (Ashburner and Friston, 2005).
SkullstrippingAnother pre-processing step is to remove all non-brain tissue, often referredto as skullstripping. This is an advanced step that itself requires localizationand delineation. A common aim of all methods is to perform the separationsomewhere in the CSF surrounding the brain while leaving the whole brainintact. The separation of brain and non-brain tissue can however be performedin slightly different locations depending on the method. There are differentexisting approaches for skullstripping. A common problem for all methods isthat small intensity bridges exist between brain and non brain tissue in T1-wimages and must be separated while leaving the brain intact.
The bridges between brain and non-brain tissue can be separated by ero-sion. In Brain Surface Extraction (BSE) described by Shattuck et al. (2001),edge detection is followed by an erosion of the inverted image, removing thebridges. The largest connected component is assumed to be the brain.
Park and Lee (2009) proposed a region growing procedure that operates inone 2D slice at a time, and expands according to multiple growing rules.
Brain Extraction Tool (BET) is another tools that fits a 3D surface meshonto the brain/non-brain border using shape and intensity information (Smith,2002). The mesh is initiated by approximating the brains center of gravity.Expansion of the mesh is performed iteratively considering mesh smoothness,mesh spacing, and likelihood of the mesh being located at an intensity corre-sponding to the brain/non-brain boundary.
A method used in the Freesurfer pipeline is the Hybrid Watershed Algo-rithm (HWA) described by Ségonne et al. (2004). A watershed transform ap-plied to an inverted T1-w image roughly delineates the brain, and a mesh isthen fitted to the boundary. To correct still remaining errors, the mesh is com-pared to an atlas containing information about expected distances from thecenter of gravity and curvature information, and large deviations are correctedfor.
A GC approach is described by Sadananthan et al. (2010). An initial thresh-olding is followed by a GC to disconnect the brain/non-brain bridges. The pos-
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sibly removed CSF and outer layer of GM is returned to the separated brainby using a morphological close operation.
4.4 AnalysisWhen delineation of a region is completed, information about intensity values,position, volume, and shape can be used in further analysis. There are how-ever some further processing required in order to compare regional volumescollected from different subjects. For some studies the volumes need to benormalized with respect to various factors in order to isolate the effects asso-ciated with the variable(s) being tested. Particularly important factors in brainanalysis are age, gender and head size (Barnes et al., 2010). The measure usedfor head size is commonly referred to as ICV, and is described in Chapter 6.
Different tissue regions in for example the cerebral cortex are associatedwith different functions. It is therefore often desired to not only analyze howthis tissue differs, but where in the tissue the difference is located.
Voxel based analysisThere is an analysis procedure called voxel-based morphometry (VBM) thatinvestigates local deviations related to some tested factor by registering allimages to a common spatial coordinate space (Ashburner and Friston, 2000).The procedure aims to compare, on a per-voxel basis, differences in the tissuebetween for example a control group and a group suspected of having changesrelated to some disease. Differences located using this method can then be vi-sualized on a brain in the common spatial coordinate space, displaying wherein the cortex an association between structural change and the tested factorexists. An established method for VBM analysis is the SPM pipeline (Ash-burner and Friston, 2005). In short, non-rigid registration to tissue probabilitymaps (TPMs), bias field estimation, and tissue classification is iteratively opti-mized until convergence. The result from an image is probabilistic classifica-tion of tissues in a known spatial space that can be used to analyze structuraldifferences on a per-voxel basis. SPM is described in more detail in Chapter 6
Regional analysisAnother approach to regional analysis is to divide a tissue, according to somemedical definition, into smaller regions of interest using a crisp delineation.As opposed to SPM where the probability of each tissue is the result, a crispdelineation assigns each voxel a specific label. A method for performing crispdelineations of smaller regions is included in the Freesurfer pipeline (Fischlet al., 2002). Here, an image is registered to an atlas using an affine transform.Rough spatial location along with intensity and neighbor information is used todivide the brain into delineated regions that can be analyzed individually. This
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method does not provide results comparing regions on a per-voxel basis, butmorphological features such as volume, shape and thickness of small regions.
4.4.1 Volumetric errorsWhen a volume has been obtained it contains an estimation error to somedegree. This error can be divided into two components, namely the systematicerror and stochastic error.
Systematic errors, commonly referred to as bias, occur due to a consistentdeviation from the true value (Fig. 4.5 a)). When having a study cohort theintroduction of a systematic error will change the mean value without heavilyaffecting the variation of the measured values. Since all values are affected thesame way, all values can be compensated with the same correction factor if itis known.
The stochastic error is random and changes from one measure to the next(Fig. 4.5 b). This error will affect a study cohort by increasing the variationbetween measurements without heavily affecting the mean value. Since thestochastic error is a sum of many random errors, it is approximately normallydistributed according to the central limit theorem as described by for exam-ple Blom (1998, p. 180–182). Therefore, the mean of multiple measurementscan be used to get a better estimation of a variable.
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0
a) b)
c)
Figure 4.5. Example of error sources a) Measuring the red object from the base ofthe ruler leads to a systematic error since the zero marking is placed a bit from therulers base. b) Measuring the red object will lead to a stochastic error since there isno marking giving the precise length of the red object, resulting in random variationsin the measurements. c) Example using a bullseye. The center bullseye shows smallsystematic and stochastic error. The one to the left shows large systematic error, andthe one to the right shows large stochastic error.
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5. Midsagittal region in image analysis
Between the cerebral hemispheres is the midsagittal region. This region con-tains many structures of interest for regional volume analysis (Fig. 3.2), andis useful to locate for spatial normalization purposes. Since a normal brainis assumed to be approximately laterally symmetric, the midsagittal region isnormally approximately flat. Important to consider however is the Yakovle-vian torque, making one hemisphere rotate around the other slightly. Thereare diseases altering these properties. A tumor can be present in one hemi-sphere but not the other. Another factor is that Schizophrenia has been seento affect the Yakovlevian torque, making it less apparent in brains affected bythis disorder (Oertel-Knöchel and Linden, 2011).
5.1 DefinitionThe most part of the midsagittal region consists of the CSF filled interhemi-spheric fissure. There are also WM fiber bundles called commissures connect-ing the hemispheres, where the largest is CC. Note that there is no consensuson where the left and right hemisphere begins and ends in this structure. Thelateral ventricles, belonging to different hemispheres, are divided by a thinmembrane called septum pellucidum which is located between CC and fornix.CC could therefore be defined as the shortest connection between septum pel-lucidum and the CSF located on the opposite side of CC.
Before leaving this section, an additional definition used in image analysisneeds to be declared. The midsagittal region can, depending on the analysis tobe performed, be estimated using simplified shapes. A plane representation ofthe midsagittal region is often referred to as the midsagittal plane (MSP). Itis commonly defined as the sagittal plane maximizing the lateral symmetry ofthe brain.
5.2 ImportanceIn image analysis, finding the midsagittal region is of importance for volumet-ric measures. Since the region contains structures that divide the lateral ventri-cles as well as the hemispheres themselves, the midsagittal region is needed forseparate volume determination of these structures. There is also the third and
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fourth ventricles along with the channel connecting the two (cerebral aque-duct) in this region, making it important for analysis of the ventricular system.
The midsagittal region contains all commissures connecting the two cere-bral hemispheres where CC is the largest one. Two smaller commissures arethe anterior commissure (AC) and posterior commissure (PC) that are oftenused for spatial normalization. Together with the MSP they define the base forthe Talairach coordinate space (Talairach and Tournoux, 1988).
5.3 Spatial normalizationA widely used spatial representation of the brain is the Talairach coordinatespace (Talairach and Tournoux, 1988). To align a brain with the Talairach at-las, three reference planes are used. The first is the MSP. The second is aplane orthogonal to MSP passing the sagittal centers of AC and PC. The thirdplane passes through AC and is orthogonal to both the other planes. A pro-portional grid coordinate system is established in relation to the three planesand a bounding box containing the brain. The Talairach atlas provides a grid-based mapping between the grid coordinates and structures. An assumptionmade in this reference system is that the interhemispheric fissure, AC and PCall lie in the MSP, i.e. the midsagittal region is flat. It is known that the mid-sagittal region can be curved and twisted (LeMay, 1976; Toga and Thompson,2003), affecting the accuracy of the coordinate system. Since the referencesystem is relative, it is also important that the relative distance between struc-tures are in consensus with the data used to create the atlas. Unfortunately thisdoes not always seem to be the case (Prakash and Nowinski, 2006). Since thetransformation into Talairach coordinates is affine, no exact mapping shouldbe expected. It is however a powerful localization tool as it can give roughpositions of all defined structures using only a few landmarks.
Locating the midsagittal region is important for spatial normalization evenif it is not included in the formal definition of a coordinate space. Registrationprocedures often involve optimization requiring a starting guess. The betterthe starting guess, the more likely and quickly the optimization is to convergeto a global optimum. Aligning the midsagittal regions of the image beingprocessed and an atlas gives a good starting guess for further optimization.
5.4 Estimation methodsThe midsagittal region is in some cases implicitly estimated as the remainderbetween separated hemispheres, and sometimes explicitly. Methods for bothimplicit and explicit midsagittal region estimation are discussed in this sec-tion. Depending on the application, the midsagittal region can be estimatedwith different shape constraints. As a preprocessing step for commissure lo-
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calization the MSP has been used successfully (Ardekani and Bachman, 2009;Nowinski et al., 2006), shape analysis can be performed using a surface (Zhaoet al., 2009), and for accurate hemispheric separation more complex shapeshave been used. It is also possible to apply different shape constraints in dif-ferent tissues to obtain an estimation (Dale et al., 1999). These methods eitherrely on symmetry or intensity information in an image.
Symmetry based planeThe midsagittal region can be estimated using a MSP when spatial normal-ization or localization of various structures is needed. Due to the curved na-ture of the midsagittal region, a MSP is not suitable for accurate division ofthe hemispheres. A common definition of the MSP is the plane with maxi-mal bi-lateral symmetry. This symmetry optimization can either be performedglobally across the image (Ardekani et al., 1997; Ruppert et al., 2011; Liuet al., 2001), or locally around the proposed plane (Hu and Nowinski, 2003;Stegmann et al., 2005; Volkau et al., 2006). By using a symmetric measurethere is no need to examine specific image intensities, making it possible toapply pure symmetry based methods on multiple image modalities. Thesemethods are in general quick and relatively easy to implement, making themhighly usable in large studies. A downside however is that they are sensitiveto brain abnormalities disturbing the symmetry between hemispheres (Fig. 5.1b).
Intensity based divisionMethods developed for more accurate localization of the midsagittal region,and accurately dividing the cerebral hemispheres, require more pre-processingand complex approaches. As the midsagittal region mostly consists of CSF, itcan be useful to perform tissue classification dividing the brain into GM, WMand CSF. Once the hemispheres have been nearly separated by locating theCSF filled interhemispheric fissure, the remaining tissue needs to be separated.
A combination of planes and tissue classification is described by Dale et al.(1999), and is used in the Freesurfer pipeline. WM is separated by fitting acutting plane to have a minimal cross-sectional area in this tissue. This typeof method needs septum pellucidum to be removed from consideration. Oth-erwise the plane cutting minimal tissue would be forced to lie next to septumpellucidum instead of following or even crossing it (Fig. 5.1 c).
Shape bottlenecks can also be used to separate WM. Paths in WM from onehemisphere to the other all pass the commissures, making them bottlenecksin the brain. Localization of these bottlenecks can be performed using simplemorphological operations (Mangin et al., 2004), or by solving a series of dif-ferential equations estimating the amount of informational flow in each voxelas seeds located in opposite hemispheres exchange information (Mangin et al.,1996; Zhao et al., 2010). Septum pellucidum is not handled by these methods.During tissue classification it is unclear if it will be labeled as a considered
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Combination methodNo single method described takes both interhemispheric structures for spatialnormalization and accurate hemisphere division into consideration. Either theimage already needs to be spatially normalized before the midsagittal regioncan be estimated, the curvature of the region is not followed, or midsagittalstructures are ignored in the estimation. Since symmetry based MSP methodshave fast execution time and have been used successfully to locate interhemi-spheric structures, they can be expanded to allow curvature where needed. Byusing symmetry information where intensity is not enough and intensity in theCSF filled interhemispheric fissure, a surface that follows the interhemisphericfissure while still "cutting" the laterally symmetric interhemispheric structurescan be extracted. Such a method was developed and evaluated in Paper I,showing strengths from both MSP and intensity based methods.
5.5 Corpus callosumPerhaps one of the most commonly investigated structures located in the mid-sagittal region is CC. This commissure has no clear lateral boundary, asit is a collection of WM fiber bundles connecting the left and right cere-bral hemispheres. There is little consensus regarding where the boundariesshould be placed when analyzing CC. The CC area in the MSP can be used asby Ardekani et al. (2013), or as in Freesurfer where CC volume is estimatedby delineation in the slice separating the cerebral hemispheres and all sliceswithin a lateral distance of this slice (Dale et al., 1999). Whichever estimateis more appropriate is unclear but to compare results of different studies it isimportant that the same definition of CC area/volume is used.
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6. Intracranial volume in image analysis
The interior volume of the human cranium is commonly referred to as ICV.Other names include cranial capacity, total intracranial volume (TIV), orestimated total intracranial volume (eTIV) as used in the analysis packageFreesurfer. Up until young adulthood the brain develops and grows. As thebrain increases in volume, ICV increases with it as the cranium closely sur-rounds the brain. At later stages in life, when the brain tissue starts to dete-riorate, ICV remains constant and does not decline with aging (Blatter et al.,1995; Courchesne et al., 2000).
In image analysis terms, ICV might seem similar to the volume obtainedwhen performing skullstripping. This is however not necessarily the case as askullstripping method generally separates brain and non-brain regions some-where in the CSF, while an ICV measure is obtained by delineating the innersurface of the cranium.
6.1 DefinitionUnfortunately, there exist no official protocol on what to include in the cal-culation of ICV. It is natural to include brain tissue and CSF, but there is noconsensus concerning the dural sinuses. The inside of the cranial bone is linedwith dura mater, one of three membranes surrounding the brain. Embeddedbetween layers of dura are the dural sinuses, that may cause an inconsistencyin ICV definition. Different medical images show different contrast betweentissues. If for instance T1-w MRI is to be used for ICV delineation, the poorcontrast between CSF and cortical bone makes it common to delineate alongthe dura mater rather than the cranial border itself (Eritaia et al., 2000; Pen-gas et al., 2009). If the inner most dural boundary is followed in the regionof the dural sinuses, the sinuses will not be included in the ICV. This mightbe preferable in cases where the sinuses are partially indistinguishable fromtheir surroundings, to prevent the inclusion of an inconsistent amount in thefollowing ICV determinations. Another difference in ICV calculations is thecranial opening where the brain stem continues down to the spinal cord. Sincethere is no bone in this region there is no natural boundary to follow.
Depending on the images used for ICV calculation, the accuracy and pro-cedure may have to vary slightly. The protocol for ICV determination used inPapers II- IV is defined as follows:
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2001) and gender differences (Scahill and Frost, 2003) in head size, enablinga better isolation of the effect desired to investigate.
6.3 Volume normalizationAs discussed in the previous section, it is important to perform ICV normal-ization in brain volumetric studies in order to remove effects of cranial sizefrom further analyses. The need for normalization is based on the assump-tion that a regional brain volume is associated with ICV to begin with. Theassociation is commonly assumed to be linear and a successful compensationfor ICV has been made when no association between the normalized regionalvolume and ICV remains (Fig. 6.2). There are multiple methods commonlyused to perform the normalization.
a) b) c)v
vnorm
v
v nor
m
ICV ICV
v = B·ICV+m vnorm = 0·ICV+X
Figure 6.2. Example of successful ICV normalization. a) Before normalization the re-gional volume (v) and ICV are linearly associated. b) The volume is normalized usingone of several available methods. c) After normalization there is no linear associationbetween vnorm and ICV, meaning an analysis can be performed without including anyeffects of subjects having different cranial size in the analysis.
6.3.1 MethodsThis subsection gives a brief summary of some established methods. They aredescribed in more detail in Paper IV. For simplified examples it is assumed thatthe relation between a regional brain volume (v) and ICV is linear accordingto v = B · ICV +m.
Proportion methodThe principle of this method is to express a regional brain volume as the pro-portion of the ICV it occupies. This is performed by simple division as inEquation 6.1.
vnorm =v
ICV(6.1)
Assuming a linear relation between v and ICV reveals that vnorm =B+m/ICV .From this it is clear that this normalization does not completely remove the
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a)
v
ICV
v
vnorm
v nor
m
ICVb)
v
ICV
v
vnorm
v nor
m
ICVc)
v
ICV
v
vnorm
v nor
m
ICV
d)
v
ICV
v
vpairs
v
ICVp1 p2 p3 p4
Figure 6.3. ICV normalization methods. a) The proportion method: Depending on theintersect m, bias can be introduced. Black line illustrates m=0. b) The residual methodnormalizing two groups separately, making vnorm = v for each respective group. c) Theresidual method normalization of two groups together leaving both groups correlatedwith ICV when inspected individually, assuming that the v-ICV association beforenormalization differs slightly between the groups. d) ICV matching, where subjectsfrom different groups matched by ICV are paired (pi) according to some criteria. Onlythe ICV overlap between the two groups can be included in the analysis, but the needfor further ICV normalization is removed.
effect of skull size from a regional brain volume unless m is equal to zero(Fig. 6.3 a). In real scenarios this is commonly not the case, and an introducederror possibly affecting the results is present when using this method.
If considering this method not as an ICV compensation method, but as away of obtaining proportional values, it might be suitable for use. This how-
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ever limits study findings from determining if a regional volume is larger inone study group than the other. It has to be clear that it is the proportions thatare studied using this method and not volumes.
Residual methodThis method aims to remove all variation in v associated with ICV. The slopeB (from the linear equation) indicates how v is expected to vary in response toa variation in ICV. Using a comparison of the subjects ICV and the mean ICVof a representative study cohort (ICV ) the normalized value can be calculatedas in Equation 6.2 (Jack et al., 1989).
vnorm = v−B(ICV − ICV ) (6.2)
Rewriting this equation using the linear association gives
vnorm = v+ ε (6.3)
where ε is added and represents the stochastic error, i.e. the measurementsrandom variation not explained by the linear association found. No associationwith ICV remains in the cohort from which B and ICV was calculated.
This method can be applied in a number of ways, and there is no way that iscorrect in all scenarios (Fig. 6.3 b–c). If for instance a regional brain volumefrom a control group is to be compared to that of a group having some disease,it might be accurate to use B and ICV from the control group to normalize theentire cohort in order to remove all "normal" variation in a volume associatedwith ICV.
When comparing regional volumes between genders, the gender groupsmight have different associations between v and ICV. Normalizing each gen-der using its own B and ICV might be tempting, but can lead to unexpectedresults (Fig. 6.3 b). Equation 6.3 shows that the normalized volume is depen-dent of v, which is smaller in females than in males if the correlation betweenv and ICV is positive (females generally have smaller ICV than males). Whenusing this normalization procedure males will therefore never have smallernormalized volumes than females under these circumstances.
Covariate methodWhen performing a statistical test it is possible to covary for ICV in a re-gression model. In other words, the variance in v explained by some otherindependent variable(s) is calculated in the same model as the variance in vassociated with ICV, making the variables compete against each other.
An example of a statistical linear regression model is given in Equation 6.4
v = β0 +β1X1 +β2X2 + ...+βmXm +βICV ICV + ε (6.4)
where Xi are independent variables included in the model, β0 is an offset, andβi is the rate of change in variable Xi associated with a change in v. Important
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when using this model is that there is no covariance between the independentvariables. If two variables explain the same variation in v, the results of Equa-tion 6.4 becomes undefined as there are many combinations of for exampleβ1X1 +β2X2 producing the same solution.
A possible model when investigating gender differences in a regional brainvolume is to have the model given in Equation 6.5,
v = β0 +βGenderGender+βICV ICV + ε (6.5)
where gender is a binary variable. In the following example it is assumed thatit is either 0 or 1. This results in the association being v = βICV ICV +β0 + ε
for one gender and v = βgender +βICV ICV +β0 + ε for the other. The modeltherefore requires a constant difference between genders equal to βgender, i.e.the slope of the linear relation between v and ICV (B from the linear equation)is equal for both genders. In real cohorts, as the one in Paper IV, this is not thecase and the model therefore contains errors.
ICV matching methodThis method aims to only use subjects that have been matched by ICV, thusremoving the need to compensate for this factor (Fig. 6.3 d). When comparingcontrols and subjects diagnosed with a disease that does not affect ICV, mostof the data might be usable. If however sexual dimorphism in regional brainvolume is to be investigated, only subjects within the ICV overlap of the gen-der groups can be used. Be aware that females with larger than average ICVare compared to males having smaller than average ICV, which might not berepresentative of the entire study cohort.
How to chooseWhen selecting a method for normalizing regional brain volumes with ICV,many factors need to be taken into consideration. A study evaluating howthe proportion and residual methods handle systematic and stochastic error(not altering the mean value) in raw volumes is presented by Sanfilipo et al.(2004). The proportion method was affected by both kinds of errors. Theresidual method was found insensitive to stochastic error in v. It was alsofound insensitive to both error types in ICV. These results are supported byEquation 6.3 where the normalized volumes are seen dependent on v and notICV. An introduction of related systematic errors in both v and ICV affectedthe residual more than the proportion method since the proportion methodcancels out the errors in some sense during division.
Arndt et al. (1991) showed that the reliability of a ratio is negatively af-fected by the two measurements involved being correlated, thus weakeningthe statistical power of a study. They also state the irony of this, as the reasonfor normalizing volumes is motivated by a correlation in the first place.
O’Brien et al. (2011) described the proportion method assuming m=0, resid-ual method using a control group to normalize the cohort, and the covariate
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method. A description of how they work and what input data can be handledwas presented, and a suggestion for visual representation of data as a step inmethod selection was made.
The selection of normalization method is not trivial, and it is problem de-pendent. What to analyze, group characteristics, measurement errors, and howthe volumes and ICV are associated all affect the method selection. When in-vestigating gender related differences in regional brain volume, please refer tothe suggested workflow for method selection presented in Paper IV.
6.3.2 When to normalizeA volume does not need to be normalized in all scenarios, and raw unnor-malized volumes might sometimes be preferable. It might for instance be ofinterest to determine if and how much smaller raw regional volumes are infemales compared to males. To determine how the variation in a volume isassociated with a variation in another volume, raw values are needed as well.Take for instance the association between regional brain volume and ICV. Ifstudies had not investigated this, the need for ICV normalization would nothave been determined.
A compensation for ICV is needed when performing inter-subject compar-isons of regional brain volume using cross-sectional data. Since the actualregional brain volumes are extracted from each subject, some compensationfor ICV is necessary as associations between regional volumes and ICV exist.When investigating volume fractions however, such as GM/WM, no compen-sation for ICV is needed since these fractions are assumed not to be associatedwith ICV.
In longitudinal studies, the need for ICV compensation is often avoided aswell, since within-subject differences are analyzed. It has however been sug-gested that an ICV normalization of longitudinal data should be performedsince method induced fluctuations in volume and ICV over time are corre-lated (Whitwell et al., 2001).
6.4 Estimation methodsIt is possible to estimate ICV from medical images showing a clear bound-ary of the inner surface of the skull. Early experiments compared the per-formance of image based estimations to the water-filling method. The water-filling method is performed by filling the cranial cavity of real skulls withwater and measure the amount required to fill the cavity (Davis and Wright,1977). Image based methods used for ICV estimation are described below.
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manual labor. Sahin et al. (2007) evaluated stereology on CT images usingthe water-filling method as reference. Stereology with 4 mm grid spacing onevery 10 mm slice was highly correlated with the reference (r=0.944) but thevolumes were significantly different, most likely due to the thick slices used.In Paper III, a comprehensive evaluation of the relation between point spacing,manual labor, and estimation error is performed.
Model estimationIf an assumption about the shape of the cranial cavity is made, ICV can beestimated by delineating a small number of slices. Eckerström et al. (2008)estimated ICV based on four segmented slices. One slice where ICA is ex-pected to be largest is chosen in the axial and coronal planes respectively. Twoslices, one on each side of the midsagittal region, were selected in the sagittaldirection and the mean area of the two was used. Based on the assumption thatthe cranial cavity can be modeled as an ellipsoid, the volume of an ellipsoidwas calculated using the square root of the three ICA as axis lengths. Thesame assumption is made by Pfefferbaum et al. (2000), but instead of usingslice areas the span of the cranium was measured. ICV estimated from a singleslice is described by Ferguson et al. (2005), where ICA of one midsagittal sliceis calculated. The correlation between this area and planimetry was r=0.976.The described models have been seen to correlate well with reference ICVmeasures, but this fact does not remove a possibility that they contain biasassociated with some important factor.
6.4.2 AutomatedTwo methods will be in focus in this subsection. These are the methods beingcompared in Paper II. SPM is a method described in Ashburner and Friston(2005), where GM, WM and CSF is estimated to calculate ICV. Freesurfer isan analysis pipeline (Dale et al., 1999; Fischl et al., 1999) using the transfor-mation determinant from an affine registration to estimate ICV (Buckner et al.,2004). Since these methods are changing with the release of newer versions,the reader should be advised that SPM implies version 8 and FS version 5.1.0if not stated otherwise.
SPMSPM uses three TPMs for the ICV estimation. These are the probabilities ofGM, WM, and CSF being located at the corresponding location. The imagesare first aligned with the TPMs using an affine transformation. An optimiza-tion procedure then iteratively updates three factors until convergence. Theseare tissue classification, bias field calculation, and registration. The currentregistration with the TPMs is used to assign each voxel to either of the threetissue classes. After this is done an update of the bias field estimation is per-formed based on the current tissue classification, and finally a deformation
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field from the registration between the image and TPMs is updated. All threeparameters are updated every iteration until the optimization converges. Allvoxels are labeled with a probability of belonging to each tissue class, withnear zero probability indicating it is likely a background voxel. By adding thethree probabilities together a probability of being inside the skull is obtained,and this value is used as an ICV estimation.
FreesurferFreesurfer registers an image with an atlas using an affine registration. Thedeterminant of the transformation matrix from the registration is a scaling fac-tor, referred to as the atlas scaling factor (ASF), indicating the relation of sizebetween the image and atlas. Scaling the ICV of the atlas with this ASF givesan estimation of ICV for the analyzed image (Eq. 6.6).
eT IV =ICVAtlas
ASF(6.6)
Another method also using an ASF derived in the same way is describedin Fein et al. (2004), where a comparison between it and a reference ICVmeasure showed good correlation. In Buckner et al. (2004) it was determinedthat an additional study specific scaling factor can be determined to achieve anestimation closer to the actual values. The benefits of having a fully automatedestimation method is however removed if manual references need to be usedto determine this additional factor, and is therefore not considered in Paper II.
ComparisonEvaluations comparing SPM and Freesurfer have been performed with mixedresults depending on the version of the evaluated methods. SPM version 5 hadless difference between longitudinal scans than Freesurfer version 3.0.2 (Pen-gas et al., 2009), and FS versions 4.5/5 were more accurate than SPM ver-sion 5 when compared to manual references (Ridgway et al., 2011). Worthmentioning is that there is a toolbox included as "Work in progress" in SPMversion 8. This toolbox called New Segment (Weiskopf et al., 2011) uses twoadditional TPMs to explicitly handle bone and non-brain soft tissue. Ridgwayet al. (2011) included this in a comparison and its ICV estimations were seenmore similar to a reference than both FS versions 4.5/5 and SPM version 5.This new toolbox is also likely to improve the accuracy of the CSF segmen-tation in Paper II. In this paper, ICV estimations from SPM version 8 and FSversion 5.1.0 were evaluated using a large reference dataset.
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algorithm (Boykov and Kolmogorov, 2004). The cost function used in GCwas based on voxel values where a high value indicated high cost. To createcosts favoring a min-cut along the interhemispheric fissure, dark CSF wascomplemented by darkening bright bi-laterally symmetric regions, to create aconnected dark path through the entire midsagittal region.
The symmetric regions were located using a two-step procedure. Firstly, alateral window was used to select voxels from each side of a voxel being ana-lyzed. An asymmetry measure was calculated by comparing the voxels fromdifferent sides, and weighted by the voxels intensity. This was performed forall voxels creating an asymmetry image (Asym). An image keeping locallyweak asymmetries (∆Asym) was calculated from Asym by using a lateral win-dow again. In the resulting ∆ Asym, bright locally symmetric maxima appearsdark while other areas are bright.
The T1-w intensity image (I) was combined with ∆Asym according to C =(I · ∆Asyma)b, where a is used to balance intensity and symmetry and b isused to balance the contrast between dark and bright areas. C then contains animage similar to I but with darkened symmetrical areas, creating a dark paththrough the interhemispheric region. C was converted to a 6-connected graph(G) with edge costs corresponding to the mean of the two adjacent voxels inC, creating low cost in dark CSF and bright symmetric regions.
Connections from the source and sink nodes to G were made under theassumption that the brain was roughly centered in the image. The further fromthe image center, the stronger the connection. In one direction the sourceconnection was increased, and in the other the sink connection was increased.
After the min-cut has been calculated, G is divided into two labeled halves.The division is performed such that the globally darkest set of edges is cut,which should be in the midsagittal region. The interhemispheric surface iscalculated as the region between all differently labeled voxels.
The method was evaluated using manually divided hemispheres and com-pared to hemisphere divisions from Freesurfer (Dale et al., 1999) and Brain-Visa (Mangin et al., 2004). The method was also compared to a midsagit-tal plane method (Hu and Nowinski, 2003) using manually placed landmarkson interhemispheric structures of interest. The lateral distance between inter-hemispheric surface/midsagittal plane and landmark was used as a measure ofhow suited a method is to be used as a pre-processing step for interhemisphericstructure localization.
7.1.3 ResultsThe developed method for interhemispheric surface extraction divides the hemi-spheres with a quality comparable to methods not regarding interhemisphericstructure localization. It is also as suitable as a pre-processing step for in-
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terhemispheric structure localization as methods not suitable for hemispheredivision.
7.1.4 ConclusionsThe method can be used as a preprocessing step for both hemispheric vol-ume estimation and structure localization. It does not rely on time consumingpre-processing nor spatial normalization, and calculates the interhemisphericsurface in a way that enables it to be used in a wide variety of studies.
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7.2 Paper IIIntracranial volume estimated with commonly used methods could introducebias in studies including brain volume measurement.
7.2.1 AimTo evaluate two commonly used methods for intracranial volume estimationusing a large reference dataset. Investigate if there are errors in the estimations,if the errors are associated with gender, skull size or atrophy, and investigateif the choice of method can affect study outcome.
7.2.2 MethodsICV estimations from Freesurfer version 5.1.0 (Buckner et al., 2004) andSPM8 (Ashburner and Friston, 2005) were calculated for 399 T1-w imagesof 75 year old subjects. Additionally, 53 subjects had an additional scan at age80 that was also processed with both methods.
PD-w images from the same subjects were used to create computer aidedreference segmentations using SmartPaint (Litjens et al., 2014; Malmberg et al.,2012). This is an interactive method based on a "smart" paintbrush that usesrelative intensity information to determine if a voxel should be painted by abrush stroke. Both inter– and intra-rater agreement was assessed using 40subjects.
ICV estimations from Freesurfer were extracted from the calculated eTIVprovided automatically. From SPM they were obtained by adding the seg-mented GM, WM, and CSF volumes.
ICV estimations were compared to the reference to investigate estimationerrors. Possible associations between estimation errors and skull size, gender,or atrophy were investigated using linear regression. Skull size was estimatedby the reference ICV, gender was 0 for males and 1 for females, and atrophywas calculated as the difference between GM+WM between 75 and 80 years.The tissue volumes used were the mean of Freesurfers and SPMs estimations.To investigate if the choice of ICV estimation method could affect study re-sults, a simple linear regression model was created. Dependent variable washippocampal volume (estimated by Freesurfer) divided by ICV estimationsfrom each respective method. Independent variables were gender, educationlevel, and a cognitive test.
7.2.3 ResultsBoth inter– and intra-rater agreement was excellent. Freesurfer and SPM bothoverestimated ICV. The estimations from Freesurfer contained errors asso-ciated with skull-size and estimation errors from SPM were associated with
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gender and atrophy. A simple example showed that the outcome of a studycan differ depending on the method used for ICV estimation.
7.2.4 ConclusionsICV estimations from commonly used methods contain errors associated withdifferent factors, and the choice of method can affect study outcome.
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7.3 Paper IIIManual intracranial volume estimation from MRI: labor reduction and esti-mation error.
7.3.1 AimTo analyze how manual labor reduction procedures for intracranial volumeestimation affect the estimation error for different degrees of labor reduction.
7.3.2 MethodsTwo procedures for manually estimating ICV from a subset of image data wereevaluated (Fig. 7.2).
Every nth slice delineation and stereology, as described in Section 6.4.1,were evaluated in terms of ICV estimation error for different degrees of la-bor reduction using the same reference segmentations as in Paper II with oneadditional segmentation for a total of 400.
To evaluate every nth slice delineation, all slice sampling densities (n) rang-ing from every slice to one slice every 60 mm were investigated. To estimateICV, the sum of all in-slice volumes was multiplied by n. The in-slice volumeis straight forward to obtain by multiplying the number of voxels inside thedelineation with the volume contained in each voxel. Stereology subsamplesthe image a bit further.
To evaluate stereology, points were placed in a square grid pattern on ev-ery selected slice, where the slices were selected in the same way as for everynth delineation. The spacing between points included in the evaluation rangedfrom 1-60 mm where whole mm were considered, disregarding the voxel di-mensions. To get the in-slice volume with this procedure, points inside thecranail cavity were counted and multiplied by the dimensions of the point gridand slice thickness.
The procedure used to subsample the images is called systematic-randomsampling, a procedure for selecting the first slice/point at random followed bya systematic placement of subsequent slices/points.
For every n there are n different ways of selecting the uppermost startingslice, and for every grid spacing (d) the upper left corner can be placed in d2
different locations. All different starting positions were used for all subjectsto calculate the estimation error with a 95% confidence level, meaning that byselecting a subject, starting slice, and grid placement at random the estimationerror will be the presented value or less 95% of the time. For every estima-tion error the mean number of segmented slices or counted points was alsocalculated.
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7.4 Paper IVIntracranial volume normalization methods: considerations when investigat-ing gender differences in regional brain volume.
7.4.1 AimGive a theoretical background to different ICV normalization methods, pro-pose a new method, and evaluate how the normalized volumes are affected bychoice of normalization method in studies investigating sexual dimorphism.
7.4.2 MethodsTo evaluate how the choice of ICV normalization method can affect studiesinvestigating gender differences in regional brain volume, a theoretical back-ground for several procedures was followed by experiments using both simu-lated and real data.
The procedures included in the evaluation were the proportion method whereregional volume is expressed as a proportion of ICV, the residual methodwhere variations in regional volume associated with ICV is removed, the co-variate method where ICV is included in a regression model as a covariate,and a matched based method that pairs subjects having similar ICV togetherfor subsequent analysis. A new matched based method was also proposed andincluded in the evaluation. The proposed method uses a weighted average tocreate inter-gender pairs matched by ICV that can be investigated for regionalbrain volume differences. Existing "match based" methods use a more directapproach when pairing, resulting in high sensitivity to variations in volumetricmeasurements.
For each procedure, gender differences in regional brain volume were as-sessed and results were compared between procedures. The experiments us-ing real data included regional volumes that have been seen to differ betweengenders previously: hippocampus, corpus callosum, white matter, and graymatter.
7.4.3 ResultsThe presented theoretical background of ICV normalization methods was con-firmed using simulated data. Depending on the method used, different resultswere obtained. This was also true when using real data. The proposed methodcan give a good visualization of the data.
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7.4.4 ConclusionsResulting gender differences were seen to depend on the normalization methodused. It is important to assess the linear relation between the regional volumethat is to be normalized and ICV before deciding on a method. The conclusionof the study resulted in a workflow suggestion for selecting ICV normalizationmethod when investigating gender differences in regional volume.
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8. Discussion
Many methods have been created to aid in the analysis of human regional brainvolume from MR images. A common procedure is to analyze an image using aprocessing pipeline applying multiple sequential methods to an image in orderto extract a region of interest. Since each processing step often relies on oneor multiple preceding steps, one less robust method included in a pipeline cangreatly affect the end results. Although an accurate extraction of a regionalbrain volume is important, a study performing ICV normalization is highlydependent on the estimation and usage of ICV as well.
8.1 PastImportant preceding work has been described throughout the thesis. Worthmentioning again are a few projects that have had a particular importance inthe field of regional volume analysis.
Perhaps the most common spatial coordinate system is that presented by Ta-lairach and Tournoux (1988), enabling the transformation of brain into a com-mon space for consistent analysis. Another widely used coordinate system, al-though not previously mentioned in this thesis, is that defined by the MNI305template (Collins et al., 1994). This template model is however, to quote theauthor, "defined in a brain-based coordinate system very similar to that pro-posed by Talairach and Tournoux (1988)", and is therefore not mentioned infurther detail in this thesis. The biggest difference is that this template is anaverage of 305 brains while the Talairach atlas is defined using a single brain.
A widely used procedure is the processing pipeline Freesurfer, enablingfully automated hemisphere division (Dale et al., 1999), skullstripping (Sé-gonne et al., 2004), ICV estimation (Buckner et al., 2004), volume estimationof many regions (Fischl et al., 2002), and more. Another widely used toolis SPM (Ashburner and Friston, 2005) providing tissue estimations, spatialnormalization, intensity non-uniformity correction, and VBM analysis. Thesetwo pipelines have enabled almost countless studies to be conducted, and asmany studies use the same analysis procedure their results are more easilycomparable.
Concerning the importance of ICV compensation the work by Davis andWright (1977), Free et al. (1995), Whitwell et al. (2001), and Scahill and Frost(2003) needs mentioning due to their investigation of how regional volumesrelate to ICV and how the compensation can reduce inter-subject variations
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and gender effects associated with subjects having differently sized heads. Er-itaia et al. (2000) and Pengas et al. (2009) investigated how ICV estimationscan be made robustly and Mathalon et al. (1993) along with Jack et al. (1989)performed important investigations how the ICV compensation can be per-formed.
8.2 PresentThis thesis will hopefully increase the quality of studies involving regionalbrain volume by methodological improvements and by highlighting importantmethodological considerations. Throughout the chapters, common methodsto analyze a regional brain volume were presented. By mentioning differentsolutions to a sub-problem, it is the authors hope to encourage method devel-opment and existing method selection based on good knowledge in the fieldof regional brain volume analysis.
When performing a study including regional brain volume there are mul-tiple important factors to consider. The regional volume estimation, the ICVestimation, and the method used to normalize the regional volume with ICV.If one of these steps contain error or is performed incorrectly, the study resultswill loose credibility even if other steps are correctly performed.
Locating the midsagittal surface has many uses, but most existing methodsestimates it in a way that limits its use. Estimating it with a symmetry basedMSP in order to locate the interhemispheric structures limits the possibilityof accurately dividing the hemispheres. When estimated in order to divide thehemispheres the interhemispheric structures are often ignored or even removedbefore the estimation. If spatial normalization occurs during the estimationthe localization of landmarks for spatial normalization looses its purpose. Thedevelopment of the method presented in Paper I was driven by a desire toextract a midsagittal surface without limiting its use.
The novelty of the method presented in Paper I lies in the use of both in-tensity and symmetry information to estimate the surface, hence the nameIntensity and Symmetry based Interhemispheric Surface extraction (ISIS). Nosuch method, to the best of the author’s knowledge, has been described previ-ously. Since symmetry based methods often are used as a pre-processing stepfor locating midsagittal structures, and intensity based methods are used to ac-curately divide the cerebral hemispheres, evaluations comparing ISIS to bothmethod categories were performed. ISIS had a performance comparable to thetested methods in their respective area of use. Another possible application forthe method, since it extracts a surface, is to analyze the curvature between thehemispheres as this has been seen related to Schizophrenia (Oertel-Knöcheland Linden, 2011).
This thesis does not provide any methodological advancement in regionalbrain volume estimation in itself, but rather focus on the evaluation of ICV
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estimations from existing methods that are commonly used in order to improvethe knowledge of these methods. In general, it appears that the neuro-scientistcommunity is focused on producing accurate regional volume estimations, butfocus less on the fact that ICV is of equal importance in brain volume studies.ICV approximations giving estimations not even correlated with a regionalvolume have been used for normalization (Sullivan et al., 2001). Since anassociation between regional volume and ICV is the base of the value used ina study (assuming ICV normalization is performed), the estimation of regionalvolume and ICV is of equal importance.
ICV is needed in many studies to isolate and remove any effects of sub-jects having differently sized heads from a regional brain volume. Multipleautomated methods for estimating ICV exists, but two were selected for de-scription in this thesis and for evaluation in Paper II. These are Freesurfer andSPM, and were selected because of their common usage in brain studies andthey also estimate regional brain volume (please refer to Section 6.4.1 for adescription of the methods). Their ICV estimations were evaluated againsta manual reference comprising 399 computer assisted segmentations. Eventhough both Freesurfer and SPM uses T1-w images to estimate ICV and re-gional brain volumes, the reference was created using PD-w images. Since theaim was to create as good ICV references as possible, PD-w images were se-lected for their superior contrast between cortical bone and CSF. Both methodsoverestimated ICV and had estimation errors associated with different factors.These associations are important to identify as they can bias study results.
The difficulty of accurate estimation of ICV is most likely due to the poorcontrast in T1-w images. This weighting is superior when estimating regionalbrain volume, but is unfortunately inferior when estimating ICV due to thepoor contrast between bone and CSF. Additional inclusion of a PD-w imageadds time and cost to a study and might not always be possible. However, asthe reproducibility of ICV estimations have been seen high when using PD-wor T2-w images (Pengas et al., 2009), methods should utilize the benefits ofPD/T2-w when available.
Seeing from Paper II how the choice of automated ICV estimations canaffect study results, the need for manual estimations is motivated. This ishowever time consuming, and not feasible in large studies. There are how-ever ways of reducing the amount of labor required to estimate ICV. Two ofthese labor reduction procedures were evaluated in Paper III. These were: tosegment every nth slice and stereology (please refer to Section 6.4.1 for a de-scription of the procedures). These two procedures are similar, but the in-slicevolume estimation differs. In every nth slice segmentation the intracranial cav-ity is delineated in the selected slice, and in stereology the in-slice volume iscalculated based on a probability. To give an alternate description of stereol-ogy to that given previously: points are placed in a grid pattern in the slice. Theprobability of a point being located inside the cranial cavity is proportional tothe percentage of the slice it occupies, i.e. if 50% of the slice contains cra-
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nial cavity, there is a 50% chance that a randomly placed point is located inthe cavity. This enables one to estimate the volume based on the number ofpoints counted inside the cranial cavity. For this assumption to be unbiased,the key is random placement. If for instance an object is on the left side of theimage and points tend to be placed on the right side, the object will be system-atically underestimated. In Paper III, systematic-random sampling was used.This means that the first point/slice is selected randomly, while all consecu-tive points/slices are placed systematically in relation to the randomly placedpoint/slice. A strength of the study is that all these estimations were performedautomatically using the previously created ICV reference, thus eliminating anyhuman error such as miscounting or inconsistent delineation. A downside isthat no measurement of time per estimation could be made.
The presented results in Paper III show how the estimation error and re-quired manual labor are affected when changing the sampling density of theprocedures. The evaluation was also performed in all three principal direc-tions. This has, to the best of the author’s knowledge, not been performedpreviously. Trying to make the results as usable as possible for future stud-ies, all possible point placements (with the limitation that they needed to beplaced on integer mm) and slice selections were analyzed to calculate an errorat a 95% confidence level.
When having obtained an estimation of ICV it is important to know howto use it. An important methodological consideration is to decide how a com-pensation for ICV should be conducted. How this method selection shouldbe performed when investigating sexual dimorphism, i.e. gender related dif-ferences in regional brain volume, was investigated in Paper IV. The genderassociated volume differences detected were seen to vary with the normaliza-tion method used. Many factors are important when considering which nor-malization method to use. For instance the linear association between ICVand regional volume and how the subjects are distributed between gendergroups. O’Brien et al. (2011) concluded that visualization of the data to benormalized is important, and the findings in Paper IV suggest the same. Thenew method presented in Paper IV could be a usable tool for this purpose.This method, when compared to traditional match based methods, includesmore subjects in the analysis and is less sensitive to random variations in themeasurements.
8.2.1 LimitationsA limitation of the method presented in Paper I is that an assumption of thebrain orientation in the images is made. In some cases the brain might bedifferently tilted and weaken the in-plane symmetry. This was not a problemin the cohort used as the images were aligned similarly throughout. It limits
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the usability in multi cohort studies however as the possibility of differentimage orientations is present.
The ICV references were created following a segmentation procedure de-rived locally with aid from a neuro-radiologist. Although it is considered thetrue ICV in this thesis, others might not agree. This might have implicationsin Paper II where references were compared to ICV estimations by Freesurferand SPM. SPM considers all tissues inside the cranial cavity to belong to GM,WM or CSF, making it hard to predict how regions like the sagittal sinus isclassified. In Freesurfer there is no actual delineation of ICV, making it im-possible to get the visual feedback necessary to determine a difference in seg-mentation procedure compared to the reference.
In Papers II and III, a limitation is that all subjects have the same age. Hav-ing subjects covering a large age span would provide results representative ofmore studies. At the same time, having subjects at a constant age was a hugebenefit in Paper IV, eliminating this factor from affecting the experiments.
The new method presented in Paper IV adds data points based on inter-polation. This results in complicated statistics as the new points need to becompensated for in a statistical test. Another limitation is that it compares,when investigating gender dimorphism, females with larger than average ICVto males with smaller than average ICV. This is however a limitation of allmatched based ICV normalization methods.
8.3 FutureOverall, more consensus in regional brain volume studies is needed. A stan-dardization of ICV segmentation protocol, the definition of regions, e.g. cor-pus callosum, and ICV normalization method would greatly increase the abil-ity to compare studies investigating similar associations. Before this can occur,studies investigating strengths and limitations of each consideration in differ-ent scenarios need to be conducted.
Addressing the limitation of Paper I, an important expansion to the mid-sagittal surface extraction method would be to allow arbitrary orientation andtilt of the input image. This would presumably increase the execution time, butallow a wider array of images to be analyzed without external reorientation ofan input image. The currently most time consuming step is the actual surfaceextraction using GC, which would be equally time consuming once initializedwith respect to image orientation. Another interesting expansion would be apost-processing step adjusting the surface. A common limitation of GC is thatit tends to "cut corners". This occurs if it is cheaper to cut a few high costedges than several low cost edges. Since the surface lied close to the real so-lution in the performed evaluations, a local adjustment could be performed byiteratively moving the surface slightly to favor an even more precise location.
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During the study performed in Paper II only 53 subjects had longitudinaldata available. Currently, more subjects have longitudinal data available mak-ing it possible to expand the analysis concerning longitudinal ICV estimationand normalization. It has for instance been suggested by Whitwell et al. (2001)that fluctuations in ICV and regional brain volume are correlated in longitu-dinal data. There was a MR scanner upgrade between the two time pointsmaking it possible to also assess effects associated to this using manually seg-mented ICV.
Since the intracranial cavity of 400 subjects have been completely delin-eated, regional differences in shape could be analyzed, further increasing theknowledge of possible error sources in ICV estimation. This could also beused to examine gender related differences in intracranial cavity, possibly de-termining a need for gender separated estimation procedures in for exampleFreesurfer and other shape dependent or affine registration based estimationmethods.
The evaluation of ICV normalization methods performed in Paper IV fo-cused on studies investigating gender differences. It would be interesting toinvestigate in detail the importance of method consideration in studies investi-gating differences between other subject categories. It is for instance commonpractice, when comparing a control group with a diagnosed group, to normal-ize both groups based on the controls variation in regional volume associatedwith ICV if the residual method is used. In the case of gender comparisons,the problem with males and females having differently sized heads exist, butin other comparisons this might not be a problem. An interesting aspect isthen that an inter-group ICV overlap could include nearly all subjects, makinga matched based normalization method overcome its limitation of excludingmany subjects.
8.4 ImplicationsThis thesis contributes to the field of regional brain volume analysis, coveringmany aspects involved in the process. A fast method for midsagittal surfaceextraction that combines strengths of multiple existing methods into one hasbeen developed, enabling it to be part of an analysis pipeline usable in manystudies. ICV estimation procedures performed both automatically and man-ually have been evaluated, contributing to the knowledge of the importanceof taking estimation procedure into consideration. The methodological con-sideration when using ICV to normalize regional brain volume has been in-vestigated, highlighting the need for careful consideration of what method toselect.
The information presented in the thesis will hopefully increase the qual-ity of future studies investigating regional brain volume by contributing withmethodological improvements and by expanding the knowledge concerning
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how methodological considerations can affect study outcome. This knowl-edge has already influenced local studies.
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Acknowledgments
Many people have contributed to this thesis and I am truly grateful to all ofyou! I would especially like to express my gratitude to the following:
My superb team of supervisors. My main supervisor Joel Kullberg forteaching me just about everything I know about science. Your time and effortinvested in me and my projects goes far beyond what I could have ever askedfor. Thank you! Elna-Marie Larsson for educating me about the brain andinvolving me in many interesting collaborations. Håkan Ahlström for havingme in your research group and for making sure my manuscripts had a niceflow. Lars Johansson for your enthusiastic explanations, story telling, andvast knowledge of how studies are to be conducted.
I would also like to thank Anders Lundberg and Gunilla Arvidsson forputting up with all my questions about PIVUS and MRI, and for collecting theimages I have used in my studies.
My gratitude to Andrew Simmons and Samantha J. Brooks for fine col-laboration and data analysis using Freesurfer and SPM.
I thank Filip Malmberg for many interesting discussions, collaborations,graph segmentation lectures, and of course SmartPaint. I also thank RobinStrand for helping me with questions about everything from manuscript for-matting to image registration.
Past and present colleagues at entrance 24 including Anders Hedström,Johan Berglund, Arvid Rudling and Gerd Nyman have my gratitude formaking this a joyful experience! I especially would like to thank Elin Lund-ström for teaching me (well I can almost say I get it) about hydrogen nucleiin the MR scanner, Christina Lundberg for helping me expand my medicalknowledge, Francisco Ortiz-Nieto for always giving me tips about interest-ing books and web pages, and Jan Weis for expanding my knowledge aboutMR and for your time spent on making the stupid printer work.
Thank you my parents Britt-Marie and Kjell and my brother Carl for shap-ing my young mind into something later capable of doing this.
Finally, my wife Ann-Sofie. I am truly blessed having you by my side. Ilove you!
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Acta Universitatis UpsaliensisDigital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Medicine 1000
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A doctoral dissertation from the Faculty of Medicine, UppsalaUniversity, is usually a summary of a number of papers. A fewcopies of the complete dissertation are kept at major Swedishresearch libraries, while the summary alone is distributedinternationally through the series Digital ComprehensiveSummaries of Uppsala Dissertations from the Faculty ofMedicine. (Prior to January, 2005, the series was publishedunder the title “Comprehensive Summaries of UppsalaDissertations from the Faculty of Medicine”.)
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