Lecture Notes in Computer Science 9349

Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

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David HutchisonLancaster University, Lancaster, UK

Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA

Josef KittlerUniversity of Surrey, Guildford, UK

Jon M. KleinbergCornell University, Ithaca, NY, USA

Friedemann MatternETH Zürich, Zürich, Switzerland

John C. MitchellStanford University, Stanford, CA, USA

Moni NaorWeizmann Institute of Science, Rehovot, Israel

C. Pandu RanganIndian Institute of Technology, Madras, India

Bernhard SteffenTU Dortmund University, Dortmund, Germany

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More information about this series at http://www.springer.com/series/7412

Nassir Navab · Joachim HorneggerWilliam M. Wells · Alejandro F. Frangi (Eds.)

Medical Image Computingand Computer-AssistedIntervention – MICCAI 201518th International ConferenceMunich, Germany, October 5–9, 2015Proceedings, Part I

ABC

EditorsNassir NavabTechnische Universität MünchenGarchingGermany

Joachim HorneggerFriedrich-Alexander-Universität

Erlangen-NürnbergErlangenGermany

William M. WellsBrigham and Women’s HospitalHarvard Medical SchoolBostonUSA

Alejandro F. FrangiUniversity of SheffieldSheffieldUK

ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Computer ScienceISBN 978-3-319-24552-2 ISBN 978-3-319-24553-9 (eBook)DOI 10.1007/978-3-319-24553-9

Library of Congress Control Number: 2015949456

LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics

Springer Cham Heidelberg New York Dordrecht Londonc© Springer International Publishing Switzerland 2015

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Preface

In 2015, the 18th International Conference on Medical Image Computing andComputer-Assisted Intervention (MICCAI 2015) was held in Munich, Germany.It was organized by the Technical University Munich (TUM) and the FriedrichAlexander University Erlangen-Nuremberg (FAU). The meeting took place inthe Philharmonic Hall “Gasteig” during October 6-8, one week after the world-famous Oktoberfest. Satellite events associated with MICCAI 2015 took placeon October 5 and October 9 in the Holiday Inn Hotel Munich City Centreand Klinikum rechts der Isar. MICCAI 2015 and its satellite events attractedword-leading scientists, engineers, and clinicians, who presented high-standardpapers, aiming at uniting the fields of medical image processing, medical imageformation, and medical robotics.

This year the triple anonymous review process was organized in severalphases. In total, 810 valid submissions were received. The review process washandled by one primary and two secondary Program Committee members foreach paper. It was initiated by the primary Program Committee member, whoassigned a minimum of three expert reviewers. Based on these initial reviews,79 papers were directly accepted and 248 papers were rejected. The remainingpapers went to the rebuttal phase, in which the authors had the chance to re-spond to the concerns raised by reviewers. The reviews and associated rebuttalswere subsequently discussed in the next phase among the reviewers leading tothe acceptance of another 85 papers and the rejection of 118 papers. Subse-quently, secondary Program Committee members issued a recommendation foreach paper by weighing both the reviewers’ recommendations and the authors’rebuttals. This resulted in “accept” for 67 papers and in “reject” for 120 papers.The remaining 92 papers were discussed at a Program Committee meeting inGarching, Germany, in May 2015, where 36 out of 75 Program Committee mem-bers were present. During two days, the 92 papers were examined by expertsin the respective fields resulting in another 32 papers being accepted. In total263 papers of the 810 submitted papers were accepted which corresponds to anacceptance rate of 32.5%.

The frequency of primary and secondary keywords is almost identical in thesubmitted, the rejected, and the accepted paper pools. The top five keywords ofall submissions were machine learning (8.3%), segmentation (7.1%), MRI (6.6%),and CAD (4.9%).

The correlation between the initial keyword counts by category and the ac-cepted papers was 0.98. The correlation with the keyword distribution of therejected papers was 0.99. The distributions of the intermediate accept and re-ject phases was also above 0.9 in all cases, i.e., there was a strong relationshipbetween the submitted paper categories and the finally accepted categories. Thekeyword frequency was essentially not influenced by the review decisions. As a

VI Preface

conclusion, we believe the review process was fair and the distribution of topicsreflects no favor of any particular topic of the conference.

This year we offered all authors the opportunity of presenting their workin a five-minute talk. These talks were organized in 11 parallel sessions settingthe stage for further scientific discussions during the poster sessions of the mainsingle-track conference. Since we consider all of the accepted papers as excellent,the selection of long oral presentations representing different fields in a singletrack was extremely challenging. Therefore, we decided to organize the papers inthese proceedings in a different way than in the conference program. In contrastto the conference program, the proceedings do not differentiate between posterand oral presentations. The proceedings are only organized by conference topics.Only for the sake of the conference program did we decide on oral and posterpresentations. In order to help us in the selection process, we asked the authors tosubmit five-minute short presentations. Based on the five-minute presentationsand the recommendations of the reviewers and Program Committee members,we selected 36 papers for oral presentation. We hope these papers to be to someextent representative of the community covering the entire MICCAI spectrum.The difference in raw review score between the poster and oral presentationswas not statistically significant (p > 0.1). In addition to the oral presentationselection process, all oral presenters were asked to submit their presentationstwo months prior to the conference for review by the Program Committee whochecked the presentations thoroughly and made suggestions for improvement.

Another feature in the conference program is the industry panel that featuresleading members of the medical software and device companies who gave theiropinions and presented their future research directions and their strategies fortranslating scientific observations and results of the MICCAI community intomedical products.

We thank Aslı Okur, who did an excellent job in the preparation of the con-ference. She took part in every detail of the organization for more than one year.We would also like to thank Andreas Maier, who supported Joachim Horneggerin his editorial tasks following his election as president of the Friedrich AlexanderUniversity Erlangen-Nuremberg (FAU) in early 2015. Furthermore, we thank thelocal Organizing Committee for arranging the wonderful venue and the MICCAIStudent Board for organizing the additional student events ranging from a tourto the BMW factory to trips to the world-famous castles of Neuschwanstein andLinderhof. The workshop, challenge, and tutorial chairs did an excellent job inenriching this year’s program. In addition, we thank the MICCAI society forprovision of support and insightful comments as well as the Program Commit-tee for their support during the review process. Last but not least, we thank oursponsors for the financial support that made the conference possible.

We look forward to seeing you in Istanbul, Turkey in 2016!

October 2015 Nassir NavabJoachim HorneggerWilliam M. Wells

Alejandro F. Frangi

Organization

General Chair

Nassir Navab Technische Universitat Munchen, Germany

General Co-chair

Joachim Hornegger Friedrich-Alexander-UniversitatErlangen-Nurnberg, Germany

Program Chairs

Nassir Navab Technische Universitat Munchen, GermanyJoachim Hornegger Friedrich-Alexander-Universitat

Erlangen-Nurnberg, GermanyWilliam M. Wells Harvard Medical School, USAAlejandro F. Frangi University of Sheffield, Sheffield, UK

Local Organization Chairs

Ralf Stauder Technische Universitat Munchen, GermanyAslı Okur Technische Universitat Munchen, GermanyPhilipp Matthies Technische Universitat Munchen, GermanyTobias Zobel Friedrich-Alexander-Universitat

Erlangen-Nurnberg, Germany

Publication Chair

Andreas Maier Friedrich-Alexander-UniversitatErlangen-Nurnberg, Germany

Sponsorship and Publicity Chairs

Stefanie Demirci Technische Universitat Munchen, GermanySu-Lin Lee Imperial College London, UK

VIII Organization

Workshop Chairs

Purang Abolmaesumi University of British Columbia, CanadaWolfgang Wein ImFusion, GermanyBertrand Thirion Inria, FranceNicolas Padoy Universite de Strasbourg, France

Challenge Chairs

Bjorn Menze Technische Universitat Munchen, GermanyLena Maier-Hein German Cancer Research Center, GermanyBram van Ginneken Radboud University, The NetherlandsValeria De Luca ETH Zurich, Switzerland

Tutorial Chairs

Tom Vercauteren University College London, UKTobias Heimann Siemens Corporate Technology, USASonia Pujol Harvard Medical School, USACarlos Alberola University of Valladolid, Spain

MICCAI Society Board of Directors

Stephen Aylward Kitware, Inc., NY, USASimon Duchesne Universite Laval, Quebec, CanadaGabor Fichtinger (Secretary) Queen’s University, Kingston, ON, CanadaAlejandro F. Frangi University of Sheffield, Sheffield, UKPolina Golland MIT, Cambridge, MA, USAPierre Jannin INSERM/INRIA, Rennes, FranceLeo Joskowicz The Hebrew University of JerusalemWiro Niessen

(Executive Director) Erasmus MC - University Medical Centre,Rotterdam, The Netherlands

Nassir Navab Technische Universitat, Munchen, GermanyAlison Noble (President) University of Oxford, Oxford, UKSebastien Ourselin (Treasurer) University College, London, UKXavier Pennec INRIA, Sophia Antipolis, FranceJosien Pluim Eindhoven University of Technology,

The NetherlandsDinggang Shen UNC, Chapel Hill, NC, USALi Shen Indiana University, IN, USA

Organization IX

MICCAI Society Consultants to the Board

Alan Colchester University of Kent, Canterbury, UKTerry Peters University of Western Ontario, London, ON,

CanadaRichard Robb Mayo Clinic College of Medicine, MN, USA

MICCAI Society Staff

Society Secretariat Janette Wallace, CanadaRecording Secretary Jackie Williams, CanadaFellows Nomination

Coordinator Terry Peters, Canada

Program Committee

Acar, BurakBarbu, AdrianBen Ayed, IsmailCastellani, UmbertoCattin, Philippe C.Chung, Albert C.S.Cootes, Timde Bruijne, MarleenDelingette, HerveFahrig, RebeccaFalcao, AlexandreFichtinger, GaborGerig, GuidoGholipour, AliGlocker, BenGreenspan, HayitHager, Gregory D.Hamarneh, GhassanHandels, HeinzHarders, MatthiasHeinrich, Mattias PaulHuang, JunzhouIonasec, RazvanIsgum, IvanaJannin, PierreJoshi, SarangJoskowicz, Leo

Kamen, AliKobashi, SyojiLangs, GeorgLi, ShuoLinguraru, Marius GeorgeLiu, HuafengLu, LeMadabhushi, AnantMaier-Hein, LenaMartel, AnneMasamune, KenMoradi, MehdiNielsen, MadsNielsen, PoulNiethammer, MarcOurselin, SebastienPadoy, NicolasPapademetris, XeniosParagios, NikosPernus, FranjoPohl, KilianPreim, BernhardPrince, JerryRadeva, PetiaRohde, GustavoSabuncu, Mert RorySakuma, Ichiro

X Organization

Salcudean, TimSalvado, OlivierSato, YoshinobuSchnabel, Julia A.Shen, LiStoyanov, DanailStudholme, ColinSyeda-Mahmood, TanveerTaylor, ZeikeUnal, GozdeVan Leemput, Koen

Wassermann, DemianWeese, JurgenWein, WolfgangWu, XiaodongYang, Guang ZhongYap, Pew-ThianYin, ZhaozhengYuan, JingZheng, GuoyanZheng, Yefeng

Additional Reviewers

Abolmaesumi, PurangAchterberg, HakimAcosta-Tamayo, OscarAerts, HugoAfacan, OnurAfsari, BijanAganj, ImanAhad, Md. Atiqur RahmanAhmidi, NargesAichert, AndreAkbari, HamedAkhondi-Asl, AlirezaAksoy, MuratAlam, SaadiaAlberola-Lopez, CarlosAljabar, PaulAllan, MaximilianAllassonnieres, StephanieAntani, SameerAntony, BhavnaArbel, TalAuvray, VincentAwate, SuyashAzzabou, NouraBagci, UlasBai, WenjiaBaka, NoraBalocco, SimoneBao, SiqiBarmpoutis, Angelos

Bartoli, AdrienBatmanghelich, KayhanBaust, MaximilianBaxter, JohnBazin, Pierre-LouisBerger, Marie-OdileBernal, JorgeBernard, OlivierBernardis, ElenaBhatia, KanwalBieth, MarieBilgic, BerkinBirkfellner, WolfgangBlaschko, MatthewBloch, IsabelleBoctor, EmadBogunovic, HrvojeBouarfa, LoubnaBouix, SylvainBourgeat, PierrickBrady, MichaelBrost, AlexanderBuerger, ChristianBurgert, OliverBurschka, DariusCaan, MatthanCahill, NathanCai, WeidongCarass, AaronCardenes, Ruben

Organization XI

Cardoso, Manuel JorgeCarmichael, OwenCaruyer, EmmanuelCathier, PascalCerrolaza, JuanCetin, MustafaCetingul, Hasan ErtanChakravarty, M. MallarChamberland, MaximeChapman, Brian E.Chatelain, PierreChen, GengChen, ShuhangChen, TingCheng, JianCheng, JunCheplygina, VeronikaChicherova, NataliaChowdhury, AnandaChristensen, GaryChui, Chee KongCinar Akakin, HaticeCinquin, PhilippeCiompi, FrancescoClarkson, MatthewClarysse, PatrickCobzas, DanaColliot, OlivierCommowick, OlivierCompas, ColinCorso, JasonCriminisi, AntonioCrum, WilliamCuingnet, RemiDaducci, AlessandroDaga, PankajDalca, AdrianDarkner, SuneDavatzikos, ChristosDawant, BenoitDehghan, EhsanDeligianni, FaniDemirci, StefanieDepeursinge, AdrienDequidt, Jeremie

Descoteaux, MaximeDeslauriers-Gauthier, SamuelDiBella, EdwardDijkstra, JoukeDing, KaiDing, XiaoweiDojat, MichelDong, XiaoDowling, JasonDowson, NicholasDu, JiaDuchateau, NicolasDuchesne, SimonDuncan, James S.Dzyubachyk, OlehEavani, HariniEbrahimi, MehranEhrhardt, JanEklund, AndersEl-Baz, AymanEl-Zehiry, NohaEllis, RandyElson, DanielErdt, MariusErnst, FlorisEslami, AbouzarFallavollita, PascalFang, RuoguFenster, AaronFeragen, AasaFick, RutgerFigl, MichaelFischer, PeterFishbaugh, JamesFletcher, P. ThomasFlorack, LucFonov, VladimirForestier, GermainFradkin, MaximFranz, AlfredFreiman, MotiFreysinger, WolfgangFripp, JurgenFrisch, BenjaminFritscher, Karl

XII Organization

Fundana, KetutGamarnik, ViktorGao, FeiGao, MingchenGao, YaozongGao, YueGaonkar, BilwajGarvin, MonaGaser, ChristianGass, TobiasGatta, CarloGeorgescu, BogdanGerber, SamuelGhesu, Florin-CristianGiannarou, StamatiaGibaud, BernardGibson, EliGilles, BenjaminGinsburg, ShoshanaGirard, GabrielGiusti, AlessandroGoh, AlvinaGoksel, OrcunGoldberger, JacobGolland, PolinaGooya, AliGrady, LeoGray, KatherineGrbic, SasaGrisan, EnricoGrova, ChristopheGubern-Merida, AlbertGuevara, PamelaGuler, Riza AlpGuo, Peifang B.Gur, YanivGutman, BorisGomez, PedroHacihaliloglu, IlkerHaidegger, TamasHajnal, JosephHamamci, AndacHammers, AlexanderHan, DongfengHargreaves, Brian

Hastreiter, PeterHatt, ChuckHawkes, DavidHayasaka, SatoruHaynor, DavidHe, HuiguangHe, TianchengHeckel, FrankHeckemann, RolfHeimann, TobiasHeng, Pheng AnnHennersperger, ChristophHolden, MatthewHong, Byung-WooHonnorat, NicolasHoogendoorn, CorneHossain, BelayatHossain, ShaheraHowe, RobertHu, YipengHuang, HengHuang, XiaojieHuang, XiaoleiHutter, JanaIbragimov, BulatIglesias, Juan EugenioIgual, LauraIordachita, IulianIrving, BenjaminJackowski, MarcelJacob, MathewsJain, AmeetJanoos, FirdausJanowczyk, AndrewJerman, TimJi, ShuiwangJi, SongbaiJiang, HaoJiang, WenchaoJiang, XiJiao, FangxiangJin, YanJolly, Marie-PierreJomier, JulienJoshi, Anand

Organization XIII

Joshi, ShantanuJung, ClaudioK.B., JayanthiKabus, SvenKadoury, SamuelKahl, FredrikKainmueller, DagmarKainz, BernhardKakadiaris, IoannisKandemir, MelihKapoor, AnkurKapur, TinaKatouzian, AminKelm, MichaelKerrien, ErwanKersten-Oertel, MartaKhan, AliKhurd, ParmeshwarKiaii, BobKikinis, RonKim, BoklyeKim, EdwardKim, MinjeongKim, SungminKing, AndrewKlein, StefanKlinder, TobiasKluckner, StefanKobayahsi, EtsukoKonukoglu, EnderKumar, PuneetKunz, ManuelaKohler, ThomasLadikos, AlexanderLandman, BennettLang, AndrewLapeer, RudyLarrabide, IgnacioLasser, TobiasLauze, FrancoisLay, NathanLe Reste, Pierre-JeanLee, Han SangLee, KangjooLee, Su-Lin

Lefkimmiatis, StamatisLefevre, JulienLekadir, KarimLelieveldt, BoudewijnLenglet, ChristopheLepore, NatashaLesage, DavidLi, GangLi, JiangLi, QuanzhengLi, XiangLi, YangLi, YeqingLiang, LiangLiao, HongenLin, HenryLindeman, RobertLindner, ClaudiaLinte, CristianLitjens, GeertLiu, FengLiu, JiaminLiu, Jian FeiLiu, JundongLiu, MingxiaLiu, SidongLiu, TianmingLiu, TingLiu, YinxiaoLombaert, HerveLorenz, CristianLorenzi, MarcoLou, XinghuaLu, YaoLuo, XiongbiaoLv, JingleiLuthi, MarcelMaass, NicoleMadooei, AliMahapatra, DwarikanathMaier, AndreasMaier-Hein (ne Fritzsche),

Klaus HermannMajumdar, AngshulMalandain, Gregoire

XIV Organization

Mansi, TommasoMansoor, AwaisMao, HongdaMao, YunxiangMarchesseau, StephanieMargeta, JanMarini Silva, RafaelMariottini, Gian LucaMarsden, AlisonMarsland, StephenMartin-Fernandez, MarcosMartı, RobertMasutani, YoshitakaMateus, DianaMcClelland, JamieMcIntosh, ChrisMedrano-Gracia, PauMeier, RaphaelMendizabal-Ruiz, E. GerardoMenegaz, GloriaMenze, BjoernMeyer, ChuckMiga, MichaelMihalef, ViorelMiller, JamesMiller, KarolMisaki, MasayaModat, MarcMoghari, MehdiMohamed, AshrafMohareri, OmidMoore, JohnMorales, Hernan G.Moreno, RodrigoMori, KensakuMorimoto, MasakazuMurphy, KeelinMuller, HenningNabavi, AryaNakajima, YoshikazuNakamura, RyoichiNapel, SandyNappi, JanneNasiriavanaki, MohammadrezaNenning, Karl-Heinz

Neumann, DominikNeumuth, ThomasNg, BernardNguyen Van, HienNicolau, stephaneNing, LipengNoble, AlisonNoble, JackNoblet, VincentO’Donnell, LaurenO’Donnell, ThomasOda, MasahiroOeltze-Jafra, SteffenOh, JunghwanOktay, Ayse BetulOkur, AslıOliver, ArnauOlivetti, EmanueleOnofrey, JohnOnogi, ShinyaOrihuela-Espina, FelipeOtake, YoshitoOu, YangmingOzarslan, EvrenPace, DaniellePapa, JoaoParsopoulos, KonstantinosPaul, PerrinePaulsen, RasmusPeng, TingyingPennec, XavierPeruzzo, DenisPeter, LoicPeterlik, IgorPetersen, JensPetitjean, CarolinePeyrat, Jean-MarcPham, DzungPiella, GemmaPitiot, AlainPizzolato, MarcoPlenge, EsbenPluim, JosienPoline, Jean-BaptistePrasad, Gautam

Organization XV

Prastawa, MarcelPratt, PhilipPreiswerk, FrankPreusser, TobiasPrevost, RaphaelPrieto, ClaudiaPunithakumar, KumaradevanPutzer, DavidQian, XiaoningQiu, WuQuellec, GwenoleRafii-Tari, HedyehRajchl, MartinRajpoot, NasirRaniga, ParneshRapaka, SaikiranRathi, YogeshRathke, FabianRauber, PauloReinertsen, IngeridReinhardt, JosephReiter, AustinRekik, IslemReyes, MauricioRicha, RogerioRieke, NicolaRiess, ChristianRiklin Raviv, TammyRisser, LaurentRit, SimonRivaz, HassanRobinson, EmmaRoche, AlexisRohling, RobertRohr, KarlRopinski, TimoRoth, HolgerRousseau, FrancoisRousson, MikaelRoy, SnehashisRueckert, DanielRueda Olarte, AndreaRuijters, DanielSamaras, DimitrisSarry, Laurent

Sato, JoaoSchaap, MichielScheinost, DustinScherrer, BenoitSchirmer, Markus D.Schmidt, FrankSchmidt-Richberg, AlexanderSchneider, CaitlinSchneider, MatthiasSchultz, ThomasSchumann, SteffenSchwartz, ErnstSechopoulos, IoannisSeiler, ChristofSeitel, AlexanderSermesant, MaximeSeshamani, SharmishtaaShahzad, RahilShamir, Reuben R.Sharma, PuneetShen, XilinShi, FengShi, KuangyuShi, WenzheShi, YinghuanShi, YonggangShin, Hoo-ChangSimpson, AmberSingh, VikasSinkus, RalphSlabaugh, GregSmeets, DirkSona, DiegoSong, YangSotiras, AristeidisSpeidel, MichaelSpeidel, StefanieSpiclin, ZigaStaib, LawrenceStamm, AymericStaring, MariusStauder, RalfStayman, J. WebsterSteinman, DavidStewart, James

XVI Organization

Styles, IainStyner, MartinSu, HangSuinesiaputra, AvanSuk, Heung-IlSummers, RonaldSun, ShanhuiSzekely, GaborSznitman, RaphaelTakeda, TakahiroTalbot, HuguesTam, RogerTamura, ManabuTang, LisaTao, LinglingTasdizen, TolgaTaylor, RussellThirion, BertrandThung, Kim-HanTiwari, PallaviToews, MatthewToh, Kim-ChuanTokuda, JunichiTong, YubingTornai, Gabor JanosTosun, DuyguTotz, JohannesTournier, J-DonaldToussaint, NicolasTraub, JoergTroccaz, JocelyneTustison, NicholasTwinanda, Andru PutraTwining, CaroleUkwatta, ErangaUmadevi Venkataraju, KannanUnay, DevrimUrschler, MartinUzunbas, MustafaVaillant, RegisVallin Spina, ThiagoVallotton, PascalVan assen, HansVan Ginneken, BramVan Tulder, Gijs

Van Walsum, TheoVandini, AlessandroVannier, MichaelVaroquaux, GaelVegas-Sanchez-Ferrero, GonzaloVenkataraman, ArchanaVercauteren, TomVeta, MtikoVignon-Clementel, IreneVillard, Pierre-FredericVisentini-Scarzanella, MarcoViswanath, SatishVitanovski, DimeVoigt, IngmarVon Berg, JensVoros, SandrineVrtovec, TomazWachinger, ChristianWaechter-Stehle, IrinaWahle, AndreasWang, AncongWang, ChaohuiWang, HaiboWang, HongzhiWang, JunchenWang, LiWang, LianshengWang, LichaoWang, LinweiWang, QianWang, QiuWang, SongWang, YalinWang, YuanquanWee, Chong-YawWei, LiuWels, MichaelWerner, ReneWesarg, StefanWestin, Carl-FredrikWhitaker, RossWhitney, JonWiles, AndrewWorz, StefanWu, Guorong

Organization XVII

Wu, HaiyongWu, Yu-ChienXie, YuchenXing, FuyongXu, YanwuXu, ZiyueXue, ZhongYagi, NaomiYamazaki, TakaharuYan, PingkunYang, LinYang, YingYao, JianhuaYaqub, MohammadYe, Dong HyeYeo, B.T. ThomasYnnermann, AndersYoung, AlistairYushkevich, PaulZacharaki, EvangeliaZacur, ErnestoZelmann, RinaZeng, WeiZhan, Liang

Zhan, YiqiangZhang, DaoqiangZhang, HuiZhang, LingZhang, MiaomiaoZhang, PeiZhang, ShaotingZhang, TianhaoZhang, TuoZhang, YongZhao, BoZhao, WeiZhijun, ZhangZhou, jinghaoZhou, LupingZhou, S. KevinZhou, YanZhu, DajiangZhu, HongtuZhu, YueminZhuang, lingZhuang, XiahaiZuluaga, Maria A.

Contents – Part I

Advanced MRI: Diffusion, fMRI, DCE

Automatic Segmentation of Renal Compartmentsin DCE-MRI Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Xin Yang, Hung Le Minh, Tim Cheng, Kyung Hyun Sung,and Wenyu Liu

Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners . . . 12Hengameh Mirzaalian, Amicie de Pierrefeu, Peter Savadjiev,Ofer Pasternak, Sylvain Bouix, Marek Kubicki, Carl-Fredrik Westin,Martha E. Shenton, and Yogesh Rathi

Track Filtering via Iterative Correction of TDI Topology . . . . . . . . . . . . . . 20Dogu Baran Aydogan and Yonggang Shi

Novel Single and Multiple Shell Uniform Sampling Schemes forDiffusion MRI Using Spherical Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Jian Cheng, Dinggang Shen, Pew-Thian Yap, and Peter J. Basser

q-Space Deep Learning for Twelve-Fold Shorter and Model-FreeDiffusion MRI Scans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Vladimir Golkov, Alexey Dosovitskiy, Philipp Samann,Jonathan I. Sperl, Tim Sprenger, Michael Czisch, Marion I. Menzel,Pedro A. Gomez, Axel Haase, Thomas Brox, and Daniel Cremers

A Machine Learning Based Approach to Fiber Tractography UsingClassifier Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Peter F. Neher, Michael Gotz, Tobias Norajitra, Christian Weber,and Klaus H. Maier-Hein

Symmetric Wiener Processes for Probabilistic Tractography andConnectivity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

M. Reisert, B. Dihtal, E. Kellner, H. Skibbe, and C. Kaller

An Iterated Complex Matrix Approach for Simulation and Analysis ofDiffusion MRI Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Hans Knutsson, Magnus Herberthson, and Carl-Fredrik Westin

Prediction of Motor Function in Very Preterm Infants UsingConnectome Features and Local Synthetic Instances . . . . . . . . . . . . . . . . . . 69

Colin J. Brown, Steven P. Miller, Brian G. Booth,Kenneth J. Poskitt, Vann Chau, Anne R. Synnes,Jill G. Zwicker, Ruth E. Grunau, and Ghassan Hamarneh

XX Contents – Part I

Segmenting Kidney DCE-MRI Using 1st-Order Shape and 5th-OrderAppearance Priors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Ni Liu, Ahmed Soliman, Georgy Gimel’farb, and Ayman El-Baz

Comparison of Stochastic and Variational Solutions to ASL fMRI DataAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Aina Frau-Pascual, Florence Forbes, and Philippe Ciuciu

Prediction of CT Substitutes from MR Images Based on Local SparseCorrespondence Combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Yao Wu, Wei Yang, Lijun Lu, Zhentai Lu, Liming Zhong, Ru Yang,Meiyan Huang, Yanqiu Feng, Wufan Chen, and Qianjin Feng

Robust Automated White Matter Pathway Reconstruction for LargeStudies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Jalmar Teeuw, Matthan W.A. Caan, and Silvia D. Olabarriaga

Exploiting the Phase in Diffusion MRI for Microstructure Recovery:Towards Axonal Tortuosity via Asymmetric Diffusion Processes . . . . . . . . 109

Marco Pizzolato, Demian Wassermann, Timothe Boutelier,and Rachid Deriche

Sparse Bayesian Inference of White Matter Fiber Orientations fromCompressed Multi-resolution Diffusion MRI . . . . . . . . . . . . . . . . . . . . . . . . . 117

Pramod Kumar Pisharady, Julio M. Duarte-Carvajalino,Stamatios N. Sotiropoulos, Guillermo Sapiro, and Christophe Lenglet

Classification of MRI under the Presence of Disease HeterogeneityUsing Multi-Task Learning: Application to Bipolar Disorder . . . . . . . . . . . 125

Xiangyang Wang, Tianhao Zhang, Tiffany M. Chaim,Marcus V. Zanetti, and Christos Davatzikos

Fiber Connection Pattern-Guided Structured Sparse Representation ofWhole-Brain fMRI Signals for Functional Network Inference . . . . . . . . . . . 133

Xi Jiang, Tuo Zhang, Qinghua Zhao, Jianfeng Lu, Lei Guo,and Tianming Liu

Joint Estimation of Hemodynamic Response Function and VoxelActivation in Functional MRI Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Priya Aggarwal, Anubha Gupta, and Ajay Garg

Quantifying Microstructure in Fiber Crossings with DiffusionalKurtosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Michael Ankele and Thomas Schultz

Which Manifold Should be Used for Group Comparison in DiffusionTensor Imaging? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

A. Bouchon, V. Noblet, F. Heitz, J. Lamy, F. Blanc,and J.-P. Armspach

Contents – Part I XXI

Convex Non-negative Spherical Factorization of Multi-shellDiffusion-Weighted Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

Daan Christiaens, Frederik Maes, Stefan Sunaert, and Paul Suetens

Tensorial Spherical Polar Fourier Diffusion MRI with OptimalDictionary Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

Jian Cheng, Dinggang Shen, Pew-Thian Yap, and Peter J. Basser

Diffusion Compartmentalization Using Response Function Groups withCardinality Penalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

Pew-Thian Yap, Yong Zhang, and Dinggang Shen

V–Bundles: Clustering Fiber Trajectories from Diffusion MRI in LinearTime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Andre Reichenbach, Mathias Goldau, Christian Heine,and Mario Hlawitschka

Assessment of Mean Apparent Propagator-Based Indices as Biomarkersof Axonal Remodeling after Stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

Lorenza Brusini, Silvia Obertino, Mauro Zucchelli,Ilaria Boscolo Galazzo, Gunnar Krueger, Cristina Granziera,and Gloria Menegaz

Integrating Multimodal Priors in Predictive Models for the FunctionalCharacterization of Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Mehdi Rahim, Bertrand Thirion, Alexandre Abraham,Michael Eickenberg, Elvis Dohmatob, Claude Comtat,and Gael Varoquaux

Simultaneous Denoising and Registration for Accurate CardiacDiffusion Tensor Reconstruction from MRI . . . . . . . . . . . . . . . . . . . . . . . . . . 215

Valeriy Vishnevskiy, Christian Stoeck, Gabor Szekely,Christine Tanner, and Sebastian Kozerke

Iterative Subspace Screening for Rapid Sparse Estimation of BrainTissue Microstructural Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Pew-Thian Yap, Yong Zhang, and Dinggang Shen

Elucidating Intravoxel Geometry in Diffusion-MRI: AsymmetricOrientation Distribution Functions (AODFs) Revealedby a Cone Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

Suheyla Cetin, Evren Ozarslan, and Gozde Unal

Modeling Task FMRI Data via Supervised Stochastic CoordinateCoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Jinglei Lv, Binbin Lin, Wei Zhang, Xi Jiang, Xintao Hu,Junwei Han, Lei Guo, Jieping Ye, and Tianming Liu

XXII Contents – Part I

Computer Assisted and Image-Guided Interventions

Autonomous Ultrasound-Guided Tissue Dissection . . . . . . . . . . . . . . . . . . . 249Philip Pratt, Archie Hughes-Hallett, Lin Zhang, Nisha Patel,Erik Mayer, Ara Darzi, and Guang-Zhong Yang

A Compact Retinal-Surgery Telemanipulator that Uses DisposableInstruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

Manikantan Nambi, Paul S. Bernstein, and Jake J. Abbott

Surgical Tool Tracking and Pose Estimation in Retinal Microsurgery . . . 266Nicola Rieke, David Joseph Tan, Mohamed Alsheakhali,Federico Tombari, Chiara Amat di San Filippo,Vasileios Belagiannis, Abouzar Eslami, and Nassir Navab

Direct Calibration of a Laser Ablation System in the Projective VoltageSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274

Adrian Schneider, Simon Pezold, Kyung-won Baek, Dilyan Marinov,and Philippe C. Cattin

Surrogate-Driven Estimation of Respiratory Motion and Layers inX-Ray Fluoroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282

Peter Fischer, Thomas Pohl, Andreas Maier, and Joachim Hornegger

Robust 5DOF Transesophageal Echo Probe Tracking at FluoroscopicFrame Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290

Charles R. Hatt, Michael A. Speidel, and Amish N. Raval

Rigid Motion Compensation in Interventional C-arm CT UsingConsistency Measure on Projection Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298

Robert Frysch and Georg Rose

Hough Forests for Real-Time, Automatic Device Localization inFluoroscopic Images: Application to TAVR . . . . . . . . . . . . . . . . . . . . . . . . . . 307

Charles R. Hatt, Michael A. Speidel, and Amish N. Raval

Hybrid Utrasound and MRI Acquisitions for High-Speed Imagingof Respiratory Organ Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

Frank Preiswerk, Matthew Toews, W. Scott Hoge,Jr-yuan George Chiou, Lawrence P. Panych,William M. Wells III, and Bruno Madore

A Portable Intra-Operative Framework Applied to Distal RadiusFracture Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

Jessica Magaraggia, Wei Wei, Markus Weiten, Gerhard Kleinszig,Sven Vetter, Jochen Franke, Karl Barth, Elli Angelopoulou,and Joachim Hornegger

Contents – Part I XXIII

Image Based Surgical Instrument Pose Estimation with Multi-classLabelling and Optical Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331

Max Allan, Ping-Lin Chang, Sebastien Ourselin, David J. Hawkes,Ashwin Sridhar, John Kelly, and Danail Stoyanov

Adaption of 3D Models to 2D X-Ray Images during EndovascularAbdominal Aneurysm Repair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339

Daniel Toth, Marcus Pfister, Andreas Maier, Markus Kowarschik,and Joachim Hornegger

Projection-Based Phase Features for Localization of a Needle Tip in 2DCurvilinear Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347

Ilker Hacihaliloglu, Parmida Beigi, Gary Ng, Robert N. Rohling,Septimiu Salcudean, and Purang Abolmaesumi

Inertial Measurement Unit for Radiation-Free Navigated ScrewPlacement in Slipped Capital Femoral Epiphysis Surgery . . . . . . . . . . . . . . 355

Bamshad Azizi Koutenaei, Ozgur Guler, Emmanuel Wilson,Matthew Oetgen, Patrick Grimm, Nassir Navab, and Kevin Cleary

Pictorial Structures on RGB-D Images for Human Pose Estimation inthe Operating Room . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363

Abdolrahim Kadkhodamohammadi, Afshin Gangi,Michel de Mathelin, and Nicolas Padoy

Interventional Photoacoustic Imaging of the Human Placenta withUltrasonic Tracking for Minimally Invasive Fetal Surgeries . . . . . . . . . . . . 371

Wenfeng Xia, Efthymios Maneas, Daniil I. Nikitichev,Charles A. Mosse, Gustavo Sato dos Santos, Tom Vercauteren,Anna L. David, Jan Deprest, Sebastien Ourselin, Paul C. Beard,and Adrien E. Desjardins

Automated Segmentation of Surgical Motion for Performance Analysisand Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379

Yun Zhou, Ioanna Ioannou, Sudanthi Wijewickrema, James Bailey,Gregor Kennedy, and Stephen O’Leary

Vision-Based Intraoperative Cone-Beam CT Stitching forNon-overlapping Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387

Bernhard Fuerst, Javad Fotouhi, and Nassir Navab

Visibility Map: A New Method in Evaluation Quality of OpticalColonoscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

Mohammad Ali Armin, Hans De Visser, Girija Chetty,Cedric Dumas, David Conlan, Florian Grimpen, and Olivier Salvado

Tissue Surface Reconstruction Aided by Local Normal InformationUsing a Self-calibrated Endoscopic Structured Light System . . . . . . . . . . . 405

Jianyu Lin, Neil T. Clancy, Danail Stoyanov, and Daniel S. Elson

XXIV Contents – Part I

Surgical Augmented Reality with Topological Changes . . . . . . . . . . . . . . . . 413Christoph J. Paulus, Nazim Haouchine, David Cazier,and Stephane Cotin

Towards an Efficient Computational Framework for Guiding SurgicalResection through Intra-operative Endo-microscopic Pathology . . . . . . . . 421

Shaohua Wan, Shanhui Sun, Subhabrata Bhattacharya,Stefan Kluckner, Alexander Gigler, Elfriede Simon,Maximilian Fleischer, Patra Charalampaki, Terrence Chen,and Ali Kamen

Automated Assessment of Surgical Skills Using Frequency Analysis . . . . . 430Aneeq Zia, Yachna Sharma, Vinay Bettadapura, Eric L. Sarin,Mark A. Clements, and Irfan Essa

Robust Live Tracking of Mitral Valve Annulus for Minimally-InvasiveIntervention Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

Ingmar Voigt, Mihai Scutaru, Tommaso Mansi, Bogdan Georgescu,Noha El-Zehiry, Helene Houle, and Dorin Comaniciu

Motion-Aware Mosaicing for Confocal Laser Endomicroscopy . . . . . . . . . . 447Jessie Mahe, Nicolas Linard, Marzieh Kohandani Tafreshi,Tom Vercauteren, Nicholas Ayache, Francois Lacombe,and Remi Cuingnet

A Registration Approach to Endoscopic Laser Speckle ContrastImaging for Intrauterine Visualisation of Placental Vessels . . . . . . . . . . . . . 455

Gustavo Sato dos Santos, Efthymios Maneas, Daniil Nikitichev,Anamaria Barburas, Anna L. David, Jan Deprest,Adrien Desjardins, Tom Vercauteren, and Sebastien Ourselin

Marker-Less AR in the Hybrid Room Using Equipment Detection forCamera Relocalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463

Nicolas Loy Rodas, Fernando Barrera, and Nicolas Padoy

Hybrid Retargeting for High-Speed Targeted Optical Biopsies . . . . . . . . . 471Andre Mouton, Menglong Ye, Francois Lacombe,and Guang-Zhong Yang

Visual Force Feedback for Hand-Held Microsurgical Instruments . . . . . . . 480Gauthier Gras, Hani J. Marcus, Christopher J. Payne, Philip Pratt,and Guang-Zhong Yang

Automated Three-Piece Digital Dental Articulation . . . . . . . . . . . . . . . . . . 488Jianfu Li, Flavio Ferraz, Shunyao Shen, Yi-Fang Lo,Xiaoyan Zhang, Peng Yuan, Zhen Tang, Ken-Chung Chen,Jaime Gateno, Xiaobo Zhou, and James J. Xia

Contents – Part I XXV

A System for MR-Ultrasound Guidance during Robot-AssistedLaparoscopic Radical Prostatectomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

Omid Mohareri, Guy Nir, Julio Lobo, Richard Savdie, Peter Black,and Septimiu Salcudean

Computer Aided Diagnosis I: Machine Learning

Automatic Fetal Ultrasound Standard Plane Detection UsingKnowledge Transferred Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . 507

Hao Chen, Qi Dou, Dong Ni, Jie-Zhi Cheng, Jing Qin, Shengli Li,and Pheng-Ann Heng

Automatic Localization and Identification of Vertebrae in Spine CT viaa Joint Learning Model with Deep Neural Networks . . . . . . . . . . . . . . . . . . 515

Hao Chen, Chiyao Shen, Jing Qin, Dong Ni, Lin Shi,Jack C.Y. Cheng, and Pheng-Ann Heng

Identification of Cerebral Small Vessel Disease Using Multiple InstanceLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523

Liang Chen, Tong Tong, Chin Pang Ho, Rajiv Patel, David Cohen,Angela C. Dawson, Omid Halse, Olivia Geraghty, Paul E.M. Rinne,Christopher J. White, Tagore Nakornchai, Paul Bentley,and Daniel Rueckert

Why Does Synthesized Data Improve Multi-sequence Classification? . . . . 531Gijs van Tulder and Marleen de Bruijne

Label Stability in Multiple Instance Learning . . . . . . . . . . . . . . . . . . . . . . . . 539Veronika Cheplygina, Lauge Sørensen, David M.J. Tax,Marleen de Bruijne, and Marco Loog

Spectral Forests: Learning of Surface Data, Application to CorticalParcellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547

Herve Lombaert, Antonio Criminisi, and Nicholas Ayache

DeepOrgan: Multi-level Deep Convolutional Networks for AutomatedPancreas Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556

Holger R. Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu,Evrim B. Turkbey, and Ronald M. Summers

3D Deep Learning for Efficient and Robust Landmark Detection inVolumetric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565

Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen,and Dorin Comaniciu

A Hybrid of Deep Network and Hidden Markov Model for MCIIdentification with Resting-State fMRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573

Heung-Il Suk, Seong-Whan Lee, and Dinggang Shen

XXVI Contents – Part I

Combining Unsupervised Feature Learning and Riesz Wavelets forHistopathology Image Representation: Application to IdentifyingAnaplastic Medulloblastoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581

Sebastian Otalora, Angel Cruz-Roa, John Arevalo, Manfredo Atzori,Anant Madabhushi, Alexander R. Judkins, Fabio Gonzalez,Henning Muller, and Adrien Depeursinge

Automatic Coronary Calcium Scoring in Cardiac CT AngiographyUsing Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589

Jelmer M. Wolterink, Tim Leiner, Max A. Viergever,and Ivana Isgum

A Random Riemannian Metric for Probabilistic Shortest-PathTractography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597

Søren Hauberg, Michael Schober, Matthew Liptrot, Philipp Hennig,and Aasa Feragen

Deep Learning and Structured Prediction for the Segmentation of Massin Mammograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605

Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley

Learning Tensor-Based Features for Whole-Brain fMRI Classification . . . 613Xiaonan Song, Lingnan Meng, Qiquan Shi, and Haiping Lu

Prediction of Trabecular Bone Anisotropy from Quantitative ComputedTomography Using Supervised Learning and a Novel MorphometricFeature Descriptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621

Vimal Chandran, Philippe Zysset, and Mauricio Reyes

Automatic Diagnosis of Ovarian Carcinomas via Sparse MultiresolutionTissue Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629

Aıcha BenTaieb, Hector Li-Chang, David Huntsman,and Ghassan Hamarneh

Scale-Adaptive Forest Training via an Efficient Feature SamplingScheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637

Loıc Peter, Olivier Pauly, Pierre Chatelain, Diana Mateus,and Nassir Navab

Multiple Instance Cancer Detection by Boosting Regularised Trees . . . . . 645Wenqi Li, Jianguo Zhang, and Stephen J. McKenna

Uncertainty-Driven Forest Predictors for Vertebra Localization andSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653

David Richmond, Dagmar Kainmueller, Ben Glocker,Carsten Rother, and Gene Myers

Who Is Talking to Whom: Synaptic Partner Detection in AnisotropicVolumes of Insect Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661

Anna Kreshuk, Jan Funke, Albert Cardona, and Fred A. Hamprecht

Contents – Part I XXVII

Direct and Simultaneous Four-Chamber Volume Estimation byMulti-Output Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669

Xiantong Zhen, Ali Islam, Mousumi Bhaduri, Ian Chan, and Shuo Li

Cross-Domain Synthesis of Medical Images Using EfficientLocation-Sensitive Deep Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677

Hien Van Nguyen, Kevin Zhou, and Raviteja Vemulapalli

Grouping Total Variation and Sparsity: Statistical Learning withSegmenting Penalties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685

Michael Eickenberg, Elvis Dohmatob, Bertrand Thirion,and Gael Varoquaux

Scandent Tree: A Random Forest Learning Method for IncompleteMultimodal Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694

Soheil Hor and Mehdi Moradi

Disentangling Disease Heterogeneity with Max-Margin MultipleHyperplane Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702

Erdem Varol, Aristeidis Sotiras, and Christos Davatzikos

Marginal Space Deep Learning: Efficient Architecture for Detection inVolumetric Image Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710

Florin C. Ghesu, Bogdan Georgescu, Yefeng Zheng,Joachim Hornegger, and Dorin Comaniciu

Nonlinear Regression on Riemannian Manifolds and Its Applications toNeuro-Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719

Monami Banerjee, Rudrasis Chakraborty, Edward Ofori,David Vaillancourt, and Baba C. Vemuri

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729

Contents – Part II

Computer Aided Diagnosis II: Automation

Computer-Aided Detection and Quantification of IntracranialAneurysms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Tim Jerman, Franjo Pernus, Bostjan Likar, and Ziga Spiclin

Discriminative Feature Selection for Multiple Ocular DiseasesClassification by Sparse Induced Graph Regularized Group Lasso . . . . . . 11

Xiangyu Chen, Yanwu Xu, Shuicheng Yan, Tat-Seng Chua,Damon Wing Kee Wong, Tien Yin Wong, and Jiang Liu

Detection of Glands and Villi by Collaboration of Domain Knowledgeand Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Jiazhuo Wang, John D. MacKenzie, Rageshree Ramachandran,and Danny Z. Chen

Minimum S-Excess Graph for Segmenting and Tracking MultipleBorders with HMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Ehab Essa, Xianghua Xie, and Jonathan-Lee Jones

Automatic Artery-Vein Separation from Thoracic CT Images UsingInteger Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Christian Payer, Michael Pienn, Zoltan Balint, Andrea Olschewski,Horst Olschewski, and Martin Urschler

Automatic Dual-View Mass Detection in Full-Field DigitalMammograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Guy Amit, Sharbell Hashoul, Pavel Kisilev, Boaz Ophir,Eugene Walach, and Aviad Zlotnick

Leveraging Mid-Level Semantic Boundary Cues for Automated LymphNode Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Ari Seff, Le Lu, Adrian Barbu, Holger Roth, Hoo-Chang Shin,and Ronald M. Summers

Computer-Aided Pulmonary Embolism Detection Using a NovelVessel-Aligned Multi-planar Image Representation and ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Nima Tajbakhsh, Michael B. Gotway, and Jianming Liang

XXX Contents – Part II

Ultrasound-Based Detection of Prostate Cancer Using AutomaticFeature Selection with Deep Belief Networks . . . . . . . . . . . . . . . . . . . . . . . . 70

Shekoofeh Azizi, Farhad Imani, Bo Zhuang, Amir Tahmasebi,Jin Tae Kwak, Sheng Xu, Nishant Uniyal, Baris Turkbey,Peter Choyke, Peter Pinto, Bradford Wood, Mehdi Moradi,Parvin Mousavi, and Purang Abolmaesumi

MCI Identification by Joint Learning on Multiple MRI Data . . . . . . . . . . . 78Yue Gao, Chong-Yaw Wee, Minjeong Kim,Panteleimon Giannakopoulos, Marie-Louise Montandon,Sven Haller, and Dinggang Shen

Medical Image Retrieval Using Multi-graph Learning for MCIDiagnostic Assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Yue Gao, Ehsan Adeli-M., Minjeong Kim,Panteleimon Giannakopoulos, Sven Haller,and Dinggang Shen

Regenerative Random Forest with Automatic Feature Selection toDetect Mitosis in Histopathological Breast Cancer Images . . . . . . . . . . . . . 94

Angshuman Paul, Anisha Dey, Dipti Prasad Mukherjee,Jayanthi Sivaswamy, and Vijaya Tourani

HoTPiG: A Novel Geometrical Feature for Vessel Morphometry and ItsApplication to Cerebral Aneurysm Detection . . . . . . . . . . . . . . . . . . . . . . . . 103

Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto,Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Kuni Ohtomo,Yoshitaka Masutani, and Akinobu Shimizu

Robust Segmentation of Various Anatomies in 3D Ultrasound UsingHough Forests and Learned Data Representations . . . . . . . . . . . . . . . . . . . . 111

Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll,Christoph Hennersperger, Federico Tombari, Amit Shah,Annika Plate, Kai Boetzel, and Nassir Navab

Improved Parkinson’s Disease Classification from Diffusion MRI Databy Fisher Vector Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Luis Salamanca, Nikos Vlassis, Nico Diederich, Florian Bernard,and Alexander Skupin

Automated Shape and Texture Analysis for Detection of Osteoarthritisfrom Radiographs of the Knee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Jessie Thomson, Terence O’Neill, David Felson, and Tim Cootes

Hashing Forests for Morphological Search and Retrieval inNeuroscientific Image Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Sepideh Mesbah, Sailesh Conjeti, Ajayrama Kumaraswamy,Philipp Rautenberg, Nassir Navab, and Amin Katouzian

Contents – Part II XXXI

Computer-Aided Infarction Identification from Cardiac CT Images:A Biomechanical Approach with SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Ken C.L. Wong, Michael Tee, Marcus Chen, David A. Bluemke,Ronald M. Summers, and Jianhua Yao

Automatic Graph-Based Localization of Cochlear Implant Electrodesin CT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Jack H. Noble and Benoit M. Dawant

Towards Non-invasive Image-Based Early Diagnosis of Autism . . . . . . . . . 160M. Mostapha, M.F. Casanova, G. Gimel’farb, and A. El-Baz

Identifying Connectome Module Patterns via New BalancedMulti-graph Normalized Cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Hongchang Gao, Chengtao Cai, Jingwen Yan, Lin Yan,Joaquin Goni Cortes, Yang Wang, Feiping Nie, John West,Andrew Saykin, Li Shen, and Heng Huang

Semantic 3-D Labeling of Ear Implants Using a Global ParametricTransition Prior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

Alexander Zouhar, Carsten Rother, and Siegfried Fuchs

Learning the Correlation Between Images and Disease Labels UsingAmbiguous Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

Tanveer Syeda-Mahmood, Ritwik Kumar, and Colin Compas

Longitudinal Analysis of Brain Recovery after Mild Traumatic BrainInjury Based on Groupwise Consistent Brain Network Clusters . . . . . . . . 194

Hanbo Chen, Armin Iraji, Xi Jiang, Jinglei Lv, Zhifeng Kou,and Tianming Liu

Registration: Method and Advanced Applications

Dictionary Learning Based Image Descriptor for MyocardialRegistration of CP-BOLD MR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Ilkay Oksuz, Anirban Mukhopadhyay, Marco Bevilacqua,Rohan Dharmakumar, and Sotirios A. Tsaftaris

Registration of Color and OCT Fundus Images Using Low-dimensionalStep Pattern Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

Jimmy Addison Lee, Jun Cheng, Guozhen Xu, Ee Ping Ong,Beng Hai Lee, Damon Wing Kee Wong, and Jiang Liu

Estimating Patient Specific Templates for Pre-operative and Follow-UpBrain Tumor Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222

Dongjin Kwon, Ke Zeng, Michel Bilello, and Christos Davatzikos

XXXII Contents – Part II

Topography-Based Registration of Developing Cortical Surfaces inInfants Using Multidirectional Varifold Representation . . . . . . . . . . . . . . . . 230

Islem Rekik, Gang Li, Weili Lin, and Dinggang Shen

A Liver Atlas Using the Special Euclidean Group . . . . . . . . . . . . . . . . . . . . 238Mohamed S. Hefny, Toshiyuki Okada, Masatoshi Hori,Yoshinobu Sato, and Randy E. Ellis

Multi-scale and Multimodal Fusion of Tract-Tracing, Myelin Stain andDTI-derived Fibers in Macaque Brains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

Tuo Zhang, Jun Kong, Ke Jing, Hanbo Chen, Xi Jiang,Longchuan Li, Lei Guo, Jianfeng Lu, Xiaoping Hu,and Tianming Liu

Space-Frequency Detail-Preserving Construction of Neonatal BrainAtlases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255

Yuyao Zhang, Feng Shi, Pew-Thian Yap, and Dinggang Shen

Mid-Space-Independent Symmetric Data Term for Pairwise DeformableImage Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263

Iman Aganj, Eugenio Iglesias, Martin Reuter, Mert R. Sabuncu,and Bruce Fischl

A 2D-3D Registration Framework for Freehand TRUS-Guided ProstateBiopsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

Siavash Khallaghi, C. Antonio Sanchez, Saman Nouranian,Samira Sojoudi, Silvia Chang, Hamidreza Abdi, Lindsay Machan,Alison Harris, Peter Black, Martin Gleave, Larry Goldenberg,S. Sidney Fels, and Purang Abolmaesumi

Unsupervised Free-View Groupwise Segmentation for M3 CardiacImages Using Synchronized Spectral Network . . . . . . . . . . . . . . . . . . . . . . . . 280

Yunliang Cai, Ali Islam, Mousumi Bhaduri, Ian Chan, and Shuo Li

Uncertainty Quantification for LDDMM Using a Low-Rank HessianApproximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289

Xiao Yang and Marc Niethammer

A Stochastic Quasi-Newton Method for Non-Rigid ImageRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297

Yuchuan Qiao, Zhuo Sun, Boudewijn P.F. Lelieveldt,and Marius Staring

Locally Orderless Registration for Diffusion Weighted Images . . . . . . . . . . 305Henrik G. Jensen, Francois Lauze, Mads Nielsen, and Sune Darkner

Predicting Activation Across Individuals with Resting-State FunctionalConnectivity Based Multi-Atlas Label Fusion . . . . . . . . . . . . . . . . . . . . . . . . 313

Georg Langs, Polina Golland, and Satrajit S. Ghosh

Contents – Part II XXXIII

Crossing-Lines Registration for Direct Electromagnetic Navigation . . . . . 321Brian J. Rasquinha, Andrew W.L. Dickinson, Gabriel Venne,David R. Pichora, and Randy E. Ellis

A Robust Outlier Elimination Approach for Multimodal Retina ImageRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

Ee Ping Ong, Jimmy Addison Lee, Jun Cheng, Guozhen Xu,Beng Hai Lee, Augustinus Laude, Stephen Teoh, Tock Han Lim,Damon W.K. Wong, and Jiang Liu

Estimating Large Lung Motion in COPD Patients by SymmetricRegularised Correspondence Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338

Mattias P. Heinrich, Heinz Handels, and Ivor J.A. Simpson

Combining Transversal and Longitudinal Registrationin IVUS Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346

G.D. Maso Talou, P.J. Blanco, I. Larrabide, C. Guedes Bezerra,P.A. Lemos, and R.A. Feijoo

Interpolation and Averaging of Multi-Compartment Model Images . . . . . 354Renaud Hedouin, Olivier Commowick, Aymeric Stamm,and Christian Barillot

Structured Decision Forests for Multi-modal Ultrasound ImageRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363

Ozan Oktay, Andreas Schuh, Martin Rajchl, Kevin Keraudren,Alberto Gomez, Mattias P. Heinrich, Graeme Penney,and Daniel Rueckert

A Hierarchical Bayesian Model for Multi-Site DiffeomorphicImage Atlases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372

Michelle Hromatka, Miaomiao Zhang, Greg M. Fleishman,Boris Gutman, Neda Jahanshad, Paul Thompson,and P. Thomas Fletcher

Distance Networks for Morphological Profiling and Characterization ofDICCCOL Landmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380

Yue Yuan, Hanbo Chen, Jianfeng Lu, Tuo Zhang, and Tianming Liu

Which Metrics Should Be Used in Non-linear RegistrationEvaluation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388

Andre Santos Ribeiro, David J. Nutt, and John McGonigle

Modeling and Simulation for Diagnosis andInterventional Planning

Illustrative Visualization of Vascular Models for Static2D Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399

Kai Lawonn, Maria Luz, Bernhard Preim, and Christian Hansen

XXXIV Contents – Part II

Perfusion Paths: Inference of Voxelwise Blood Flow Trajectories in CTPerfusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

David Robben, Stefan Sunaert, Vincent Thijs, Guy Wilms,Frederik Maes, and Paul Suetens

Automatic Prostate Brachytherapy Preplanning Using Joint SparseAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

Saman Nouranian, Mahdi Ramezani, Ingrid Spadinger,William J. Morris, Septimiu E. Salcudean, and Purang Abolmaesumi

Bayesian Personalization of Brain Tumor Growth Model . . . . . . . . . . . . . . 424Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer,Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach,and Nicholas Ayache

Learning Patient-Specific Lumped Models for Interactive CoronaryBlood Flow Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433

Hannes Nickisch, Yechiel Lamash, Sven Prevrhal, Moti Freiman,Mani Vembar, Liran Goshen, and Holger Schmitt

Vito – A Generic Agent for Multi-physics Model Personalization:Application to Heart Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442

Dominik Neumann, Tommaso Mansi, Lucian Itu,Bogdan Georgescu, Elham Kayvanpour, Farbod Sedaghat-Hamedani,Jan Haas, Hugo Katus, Benjamin Meder, Stefan Steidl,Joachim Hornegger, and Dorin Comaniciu

Database-Based Estimation of Liver Deformation underPneumoperitoneum for Surgical Image-Guidance and Simulation . . . . . . . 450

S.F. Johnsen, S. Thompson, M.J. Clarkson, M. Modat, Y. Song,J. Totz, K. Gurusamy, B. Davidson, Z.A. Taylor, D.J. Hawkes,and S. Ourselin

Computational Sonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Christoph Hennersperger, Maximilian Baust, Diana Mateus,and Nassir Navab

Hierarchical Shape Distributions for Automatic Identification of 3DDiastolic Vortex Rings from 4D Flow MRI . . . . . . . . . . . . . . . . . . . . . . . . . . 467

Mohammed S.M. Elbaz, Boudewijn P.F. Lelieveldt,and Rob J. van der Geest

Robust CT Synthesis for Radiotherapy Planning: Application to theHead and Neck Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476

Ninon Burgos, M. Jorge Cardoso, Filipa Guerreiro, Catarina Veiga,Marc Modat, Jamie McClelland, Antje-Christin Knopf,Shonit Punwani, David Atkinson, Simon R. Arridge,Brian F. Hutton, and Sebastien Ourselin

Contents – Part II XXXV

Mean Aneurysm Flow Amplitude Ratio Comparison between DSA andCFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485

Fred van Nijnatten, Odile Bonnefous, Hernan G. Morales,Thijs Grunhagen, Roel Hermans, Olivier Brina,Vitor Mendes Pereira, and Daniel Ruijters

Application of L0-Norm Regularization to Epicardial PotentialReconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

Liansheng Wang, Xinyue Li, Yiping Chen, and Jing Qin

MRI-Based Lesion Profiling of Epileptogenic Cortical Malformations . . . 501Seok-Jun Hong, Boris C. Bernhardt, Dewi Schrader,Benoit Caldairou, Neda Bernasconi, and Andrea Bernasconi

Patient-specific 3D Ultrasound Simulation Based on ConvolutionalRay-tracing and Appearance Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 510

Mehrdad Salehi, Seyed-Ahmad Ahmadi, Raphael Prevost,Nassir Navab, and Wolfgang Wein

Robust Transmural Electrophysiological Imaging: Integrating Sparseand Dynamic Physiological Models into ECG-Based Inference . . . . . . . . . 519

Jingjia Xu, John L. Sapp, Azar Rahimi Dehaghani, Fei Gao,Milan Horacek, and Linwei Wang

Estimating Biophysical Parameters from BOLD Signals throughEvolutionary-Based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528

Pablo Mesejo, Sandrine Saillet, Olivier David, Christian Benar,Jan M. Warnking, and Florence Forbes

Radiopositive Tissue Displacement Compensation for SPECT-guidedSurgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536

Francisco Pinto, Bernhard Fuerst, Benjamin Frisch,and Nassir Navab

A Partial Domain Approach to Enable Aortic Flow Simulation WithoutTurbulent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544

Taha S. Koltukluoglu, Christian Binter, Christine Tanner,Sven Hirsch, Sebastian Kozerke, Gabor Szekely,and Aymen Laadhari

Reconstruction, Image Formation, AdvancedAcquisition - Computational Imaging

Flexible Reconstruction and Correction of Unpredictable Motion fromStacks of 2D Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555

Bernhard Kainz, Amir Alansary, Christina Malamateniou,Kevin Keraudren, Mary Rutherford, Joseph V. Hajnal,and Daniel Rueckert

XXXVI Contents – Part II

Efficient Preconditioning in Joint Total Variation Regularized ParallelMRI Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563

Zheng Xu, Yeqing Li, Leon Axel, and Junzhou Huang

Accessible Digital Ophthalmoscopy Based on Liquid-Lens Technology . . . 571Christos Bergeles, Pierre Berthet-Rayne, Philip McCormac,Luis C. Garcia-Peraza-Herrera, Kosy Onyenso, Fan Cao,Khushi Vyas, Melissa Berthelot, and Guang-Zhong Yang

Estimate, Compensate, Iterate: Joint Motion Estimation andCompensation in 4-D Cardiac C-arm Computed Tomography . . . . . . . . . . 579

Oliver Taubmann, Gunter Lauritsch, Andreas Maier,Rebecca Fahrig, and Joachim Hornegger

Robust Prediction of Clinical Deep Brain Stimulation Target Structuresvia the Estimation of Influential High-Field MR Atlases . . . . . . . . . . . . . . . 587

Jinyoung Kim, Yuval Duchin, Hyunsoo Kim, Jerrold Vitek,Noam Harel, and Guillermo Sapiro

RF Ultrasound Distribution-Based Confidence Maps . . . . . . . . . . . . . . . . . 595Tassilo Klein and William M. Wells III

Prospective Prediction of Thin-Cap Fibroatheromas from BaselineVirtual Histology Intravascular Ultrasound Data . . . . . . . . . . . . . . . . . . . . . 603

Ling Zhang, Andreas Wahle, Zhi Chen, John Lopez,Tomas Kovarnik, and Milan Sonka

Cooperative Robotic Gamma Imaging: Enhancing US-guided NeedleBiopsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

Marco Esposito, Benjamin Busam, Christoph Hennersperger,Julia Rackerseder, An Lu, Nassir Navab, and Benjamin Frisch

Bayesian Tomographic Reconstruction Using Riemannian MCMC . . . . . . 619Stefano Pedemonte, Ciprian Catana, and Koen Van Leemput

A Landmark-Based Approach for Robust Estimation of Exposure IndexValues in Digital Radiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627

Paolo Irrera, Isabelle Bloch, and Maurice Delplanque

Accelerated Dynamic MRI Reconstruction with Total Variation andNuclear Norm Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635

Jiawen Yao, Zheng Xu, Xiaolei Huang, and Junzhou Huang

Robust PET Motion Correction Using Non-local Spatio-temporalPriors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643

S. Thiruvenkadam, K.S. Shriram, R. Manjeshwar,and S.D. Wollenweber

Contents – Part II XXXVII

Subject-specific Models for the Analysis of PathologicalFDG PET Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651

Ninon Burgos, M. Jorge Cardoso, Alex F. Mendelson,Jonathan M. Schott, David Atkinson, Simon R. Arridge,Brian F. Hutton, and Sebastien Ourselin

Hierarchical Reconstruction of 7T-like Images from 3T MRI UsingMulti-level CCA and Group Sparsity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659

Khosro Bahrami, Feng Shi, Xiaopeng Zong, Hae Won Shin,Hongyu An, and Dinggang Shen

Robust Spectral Denoising for Water-Fat Separation in MagneticResonance Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667

Felix Lugauer, Dominik Nickel, Jens Wetzl,Stephan A.R. Kannengiesser, Andreas Maier,and Joachim Hornegger

Scale Factor Point Spread Function Matching: Beyond Aliasing inImage Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675

M. Jorge Cardoso, Marc Modat, Tom Vercauteren,and Sebastien Ourselin

Analytic Quantification of Bias and Variance of Coil Sensitivity ProfileEstimators for Improved Image Reconstruction in MRI . . . . . . . . . . . . . . . 684

Aymeric Stamm, Jolene Singh, Onur Afacan,and Simon K. Warfield

Mobile C-arm 3D Reconstruction in the Presence of UncertainGeometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 692

Caleb Rottman, Lance McBride, Arvidas Cheryauka, Ross Whitaker,and Sarang Joshi

Fast Preconditioning for Accelerated Multi-contrast MRIReconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700

Ruoyu Li, Yeqing Li, Ruogu Fang, Shaoting Zhang, Hao Pan,and Junzhou Huang

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709

Contents – Part III

Quantitative Image Analysis I: Segmentation andMeasurement

Deep Convolutional Encoder Networks for Multiple Sclerosis LesionSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Tom Brosch, Youngjin Yoo, Lisa Y.W. Tang, David K.B. Li,Anthony Traboulsee, and Roger Tam

Unsupervised Myocardial Segmentation for Cardiac MRI . . . . . . . . . . . . . . 12Anirban Mukhopadhyay, Ilkay Oksuz, Marco Bevilacqua,Rohan Dharmakumar, and Sotirios A. Tsaftaris

Multimodal Cortical Parcellation Based on Anatomical and FunctionalBrain Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Chendi Wang, Burak Yoldemir, and Rafeef Abugharbieh

Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta inFetal MRI Using One-Plane Scribbles and Online Learning . . . . . . . . . . . . 29

Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen,Anna L. David, Jan Deprest, Tom Vercauteren,and Sebastien Ourselin

GPSSI: Gaussian Process for Sampling Segmentations of Images . . . . . . . 38Matthieu Le, Jan Unkelbach, Nicholas Ayache, and Herve Delingette

Multi-level Parcellation of the Cerebral Cortex Using Resting-StatefMRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Salim Arslan and Daniel Rueckert

Interactive Multi-organ Segmentation Based on Multiple TemplateDeformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Romane Gauriau, David Lesage, Melanie Chiaradia, Baptiste Morel,and Isabelle Bloch

Segmentation of Infant Hippocampus Using Common FeatureRepresentations Learned for Multimodal Longitudinal Data . . . . . . . . . . . 63

Yanrong Guo, Guorong Wu, Pew-Thian Yap, Valerie Jewells,Weili Lin, and Dinggang Shen

Measuring Cortical Neurite-Dispersion and Perfusion in Preterm-BornAdolescents Using Multi-modal MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Andrew Melbourne, Zach Eaton-Rosen, David Owen, Jorge Cardoso,Joanne Beckmann, David Atkinson, Neil Marlow,and Sebastien Ourselin

XL Contents – Part III

Interactive Whole-Heart Segmentation in Congenital Heart Disease . . . . . 80Danielle F. Pace, Adrian V. Dalca, Tal Geva, Andrew J. Powell,Mehdi H. Moghari, and Polina Golland

Automatic 3D US Brain Ventricle Segmentation in Pre-Term NeonatesUsing Multi-phase Geodesic Level-Sets with Shape Prior . . . . . . . . . . . . . . 89

Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen,Martin Rajchl, Eranga Ukwatta, Sandrine de Ribaupierre,and Aaron Fenster

Multiple Surface Segmentation Using Truncated Convex Priors . . . . . . . . 97Abhay Shah, Junjie Bai, Zhihong Hu, Srinivas Sadda,and Xiaodong Wu

Statistical Power in Image Segmentation: Relating Sample Size toReference Standard Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Eli Gibson, Henkjan J. Huisman, and Dean C. Barratt

Joint Learning of Image Regressor and Classifier for DeformableSegmentation of CT Pelvic Organs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Yaozong Gao, Jun Lian, and Dinggang Shen

Corpus Callosum Segmentation in MS Studies Using Normal Atlasesand Optimal Hybridization of Extrinsic and Intrinsic Image Cues . . . . . . 123

Lisa Y.W. Tang, Ghassan Hamarneh, Anthony Traboulsee,David Li, and Roger Tam

Brain Tissue Segmentation Based on Diffusion MRI Using �0Sparse-Group Representation Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Pew-Thian Yap, Yong Zhang, and Dinggang Shen

A Latent Source Model for Patch-Based Image Segmentation . . . . . . . . . . 140George H. Chen, Devavrat Shah, and Polina Golland

Multi-organ Segmentation Using Shape Model Guided Local PhaseAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Chunliang Wang and Orjan Smedby

Filling Large Discontinuities in 3D Vascular Networks UsingSkeleton- and Intensity-Based Information . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Russell Bates, Laurent Risser, Benjamin Irving,Bart�lomiej W. Papiez, Pavitra Kannan, Veerle Kersemans,and Julia A. Schnabel

A Continuous Flow-Maximisation Approach to Connectivity-DrivenCortical Parcellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

Sarah Parisot, Martin Rajchl, Jonathan Passerat-Palmbach,and Daniel Rueckert

Contents – Part III XLI

A 3D Fractal-Based Approach Towards Understanding Changes in theInfarcted Heart Microvasculature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

Polyxeni Gkontra, Magdalena M. Zak, Kerri-Ann Norton,Andres Santos, Aleksander S. Popel, and Alicia G. Arroyo

Segmenting the Uterus in Monocular Laparoscopic Images withoutManual Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

Toby Collins, Adrien Bartoli, Nicolas Bourdel, and Michel Canis

Progressive Label Fusion Framework for Multi-atlas Segmentation byDictionary Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

Yantao Song, Guorong Wu, Quansen Sun, Khosro Bahrami,Chunming Li, and Dinggang Shen

Multi-atlas Based Segmentation Editing with Interaction-GuidedConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

Sang Hyun Park, Yaozong Gao, and Dinggang Shen

Quantitative Image Analysis II: Microscopy,Fluorescence and Histological Imagery

Improving Convenience and Reliability of 5-ALA-Induced FluorescentImaging for Brain Tumor Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

Hiroki Taniguchi, Noriko Kohira, Takashi Ohnishi,Hiroshi Kawahira, Mikael von und zu Fraunberg,Juha E. Jaaskelainen, Markku Hauta-Kasari, Yasuo Iwadate,and Hideaki Haneishi

Analysis of High-Throughput Microscopy Videos: Catching Up withCell Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

A. Arbelle, N. Drayman, M. Bray, U. Alon, A. Carpenter,and T. Riklin Raviv

Neutrophils Identification by Deep Learning and Voronoi Diagram ofClusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226

Jiazhuo Wang, John D. MacKenzie, Rageshree Ramachandran,and Danny Z. Chen

U-Net: Convolutional Networks for Biomedical Image Segmentation . . . . 234Olaf Ronneberger, Philipp Fischer, and Thomas Brox

Co-restoring Multimodal Microscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . 242Mingzhong Li and Zhaozheng Yin

A 3D Primary Vessel Reconstruction Framework with Serial MicroscopyImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

Yanhui Liang, Fusheng Wang, Darren Treanor, Derek Magee,George Teodoro, Yangyang Zhu, and Jun Kong

XLII Contents – Part III

Adaptive Co-occurrence Differential Texton Space for HEp-2 CellsClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Xiang Xu, Feng Lin, Carol Ng, and Khai Pang Leong

Learning to Segment: Training Hierarchical Segmentation under aTopological Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268

Jan Funke, Fred A. Hamprecht, and Chong Zhang

You Should Use Regression to Detect Cells . . . . . . . . . . . . . . . . . . . . . . . . . . 276Philipp Kainz, Martin Urschler, Samuel Schulter, Paul Wohlhart,and Vincent Lepetit

A Hybrid Approach for Segmentation and Tracking of MyxococcusXanthus Swarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284

Jianxu Chen, Shant Mahserejian, Mark Alber, and Danny Z. Chen

Fast Background Removal in 3D Fluorescence Microscopy Images UsingOne-Class Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292

Lin Yang, Yizhe Zhang, Ian H. Guldner, Siyuan Zhang,and Danny Z. Chen

Motion Representation of Ciliated Cell Images with Contour-Alignmentfor Automated CBF Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

Fan Zhang, Yang Song, Siqi Liu, Paul Young, Daniela Traini,Lucy Morgan, Hui-Xin Ong, Lachlan Buddle, Sidong Liu,Dagan Feng, and Weidong Cai

Multimodal Dictionary Learning and Joint Sparse Representation forHEp-2 Cell Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

Ali Taalimi, Shahab Ensafi, Hairong Qi, Shijian Lu,Ashraf A. Kassim, and Chew Lim Tan

Cell Event Detection in Phase-Contrast Microscopy Sequences fromFew Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

Melih Kandemir, Christian Wojek, and Fred A. Hamprecht

Robust Muscle Cell Quantification Using Structured Edge Detectionand Hierarchical Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324

Fujun Liu, Fuyong Xing, Zizhao Zhang, Mason Mcgough,and Lin Yang

Fast Cell Segmentation Using Scalable Sparse Manifold Learning andAffine Transform-Approximated Active Contour . . . . . . . . . . . . . . . . . . . . . 332

Fuyong Xing and Lin Yang

Restoring the Invisible Details in Differential Interference ContrastMicroscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340

Wenchao Jiang and Zhaozheng Yin

Contents – Part III XLIII

A Novel Cell Detection Method Using Deep Convolutional NeuralNetwork and Maximum-Weight Independent Set . . . . . . . . . . . . . . . . . . . . . 349

Fujun Liu and Lin Yang

Beyond Classification: Structured Regression for Robust Cell DetectionUsing Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358

Yuanpu Xie, Fuyong Xing, Xiangfei Kong, Hai Su, and Lin Yang

Joint Kernel-Based Supervised Hashing for Scalable HistopathologicalImage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366

Menglin Jiang, Shaoting Zhang, Junzhou Huang, Lin Yang,and Dimitris N. Metaxas

Deep Voting: A Robust Approach Toward Nucleus Localization inMicroscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374

Yuanpu Xie, Xiangfei Kong, Fuyong Xing, Fujun Liu, Hai Su,and Lin Yang

Robust Cell Detection and Segmentation in Histopathological ImagesUsing Sparse Reconstruction and Stacked Denoising Autoencoders . . . . . 383

Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang,and Lin Yang

Automatic in Vivo Cell Detection in MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . 391Muhammad Jamal Afridi, Xiaoming Liu, Erik Shapiro,and Arun Ross

Quantitative Image Analysis III: Motion,Deformation, Development and Degeneration

Automatic Vessel Segmentation from Pulsatile Radial Distension . . . . . . . 403Alborz Amir-Khalili, Ghassan Hamarneh, and Rafeef Abugharbieh

A Sparse Bayesian Learning Algorithm for Longitudinal Image Data . . . . 411Mert R. Sabuncu

Descriptive and Intuitive Population-Based Cardiac Motion Analysisvia Sparsity Constrained Tensor Decomposition . . . . . . . . . . . . . . . . . . . . . . 419

K. McLeod, M. Sermesant, P. Beerbaum, and X. Pennec

Liver Motion Estimation via Locally Adaptive Over-SegmentationRegularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427

Bart�lomiej W. Papiez, Jamie Franklin, Mattias P. Heinrich,Fergus V. Gleeson, and Julia A. Schnabel

Motion-Corrected, Super-Resolution Reconstruction for High-Resolution 3D Cardiac Cine MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435

Freddy Odille, Aurelien Bustin, Bailiang Chen,Pierre-Andre Vuissoz, and Jacques Felblinger

XLIV Contents – Part III

Motion Estimation of Common Carotid Artery Wall Using a H∞ FilterBased Block Matching Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443

Zhifan Gao, Huahua Xiong, Heye Zhang, Dan Wu, Minhua Lu,Wanqing Wu, Kelvin K.L. Wong, and Yuan-Ting Zhang

Gated-Tracking: Estimation of Respiratory Motion with Confidence . . . . 451Valeria De Luca, Gabor Szekely, and Christine Tanner

Solving Logistic Regression with Group Cardinality Constraints forTime Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459

Yong Zhang and Kilian M. Pohl

Spatiotemporal Parsing of Motor Kinematics for Assessing StrokeRecovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467

Borislav Antic, Uta Buchler, Anna-Sophia Wahl, Martin E. Schwab,and Bjorn Ommer

Longitudinal Analysis of Pre-term Neonatal Brain Ventricle inUltrasound Images Based on Convex Optimization . . . . . . . . . . . . . . . . . . . 476

Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen,Martin Rajchl, Eranga Ukwatta, Sandrine de Ribaupierre,and Aaron Fenster

Multi-GPU Reconstruction of Dynamic Compressed Sensing MRI . . . . . . 484Tran Minh Quan, Sohyun Han, Hyungjoon Cho, and Won-Ki Jeong

Prospective Identification of CRT Super Responders Using a MotionAtlas and Random Projection Ensemble Learning . . . . . . . . . . . . . . . . . . . . 493

Devis Peressutti, Wenjia Bai, Thomas Jackson, Manav Sohal,Aldo Rinaldi, Daniel Rueckert, and Andrew King

Motion Compensated Abdominal Diffusion Weighted MRI bySimultaneous Image Registration and Model Estimation (SIR-ME) . . . . . 501

Sila Kurugol, Moti Freiman, Onur Afacan, Liran Domachevsky,Jeannette M. Perez-Rossello, Michael J. Callahan,and Simon K. Warfield

Fast Reconstruction of Accelerated Dynamic MRI Using ManifoldKernel Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510

Kanwal K. Bhatia, Jose Caballero, Anthony N. Price, Ying Sun,Jo V. Hajnal, and Daniel Rueckert

Predictive Modeling of Anatomy with Genetic and Clinical Data . . . . . . . 519Adrian V. Dalca, Ramesh Sridharan, Mert R. Sabuncu,and Polina Golland

Contents – Part III XLV

Joint Diagnosis and Conversion Time Prediction of Progressive MildCognitive Impairment (pMCI) Using Low-Rank Subspace Clusteringand Matrix Completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

Kim-Han Thung, Pew-Thian Yap, Ehsan Adeli-M.,and Dinggang Shen

Learning with Heterogeneous Data for Longitudinal Studies . . . . . . . . . . . 535Letizia Squarcina, Cinzia Perlini, Marcella Bellani,Antonio Lasalvia, Mirella Ruggeri, Paolo Brambilla,and Umberto Castellani

Parcellation of Infant Surface Atlas Using Developmental Trajectoriesof Multidimensional Cortical Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543

Gang Li, Li Wang, John H. Gilmore, Weili Lin, and Dinggang Shen

Graph-Based Motion-Driven Segmentation of the CarotidAtherosclerotique Plaque in 2D Ultrasound Sequences . . . . . . . . . . . . . . . . 551

Aimilia Gastounioti, Aristeidis Sotiras, Konstantina S. Nikita,and Nikos Paragios

Cortical Surface-Based Construction of Individual Structural Networkwith Application to Early Brain Development Study . . . . . . . . . . . . . . . . . . 560

Yu Meng, Gang Li, Weili Lin, John H. Gilmore, and Dinggang Shen

Quantitative Image Analysis IV: Classification,Detection, Features, and Morphology

NEOCIVET: Extraction of Cortical Surface and Analysis of NeonatalGyrification Using a Modified CIVET Pipeline . . . . . . . . . . . . . . . . . . . . . . . 571

Hosung Kim, Claude Lepage, Alan C. Evans, A. James Barkovich,and Duan Xu

Learning-Based Shape Model Matching: Training Accurate Modelswith Minimal Manual Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580

Claudia Lindner, Jessie Thomson, The arcOGEN Consortium,and Tim F. Cootes

Scale and Curvature Invariant Ridge Detector for Tortuous andFragmented Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

Roberto Annunziata, Ahmad Kheirkhah, Pedram Hamrah,and Emanuele Trucco

Boosting Hand-Crafted Features for Curvilinear Structure Segmentationby Learning Context Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596

Roberto Annunziata, Ahmad Kheirkhah, Pedram Hamrah,and Emanuele Trucco

XLVI Contents – Part III

Kernel-Based Analysis of Functional Brain Connectivity on GrassmannManifold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604

Luca Dodero, Fabio Sambataro, Vittorio Murino, and Diego Sona

A Steering Engine: Learning 3-D Anatomy Orientation UsingRegression Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612

Fitsum A. Reda, Yiqiang Zhan, and Xiang Sean Zhou

Automated Localization of Fetal Organs in MRI Using Random Forestswith Steerable Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620

Kevin Keraudren, Bernhard Kainz, Ozan Oktay,Vanessa Kyriakopoulou, Mary Rutherford, Joseph V. Hajnal,and Daniel Rueckert

A Statistical Model for Smooth Shapes in Kendall Shape Space . . . . . . . . 628Akshay V. Gaikwad, Saurabh J. Shigwan, and Suyash P. Awate

A Cross Saliency Approach to Asymmetry-Based Tumor Detection . . . . . 636Miri Erihov, Sharon Alpert, Pavel Kisilev, and Sharbell Hashoul

Grey Matter Sublayer Thickness Estimation in the Mouse Cerebellum . . 644Da Ma, Manuel J. Cardoso, Maria A. Zuluaga, Marc Modat,Nick Powell, Frances Wiseman, Victor Tybulewicz, Elizabeth Fisher,Mark F. Lythgoe, and Sebastien Ourselin

Unregistered Multiview Mammogram Analysis with Pre-trained DeepLearning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652

Gustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradley

Automatic Craniomaxillofacial Landmark Digitization viaSegmentation-Guided Partially-Joint Regression Forest Model . . . . . . . . . 661

Jun Zhang, Yaozong Gao, Li Wang, Zhen Tang, James J. Xia,and Dinggang Shen

Automatic Feature Learning for Glaucoma Detection Based on DeepLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669

Xiangyu Chen, Yanwu Xu, Shuicheng Yan,Damon Wing Kee Wong, Tien Yin Wong,and Jiang Liu

Fast Automatic Vertebrae Detection and Localization in PathologicalCT Scans - A Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678

Amin Suzani, Alexander Seitel, Yuan Liu, Sidney Fels,Robert N. Rohling, and Purang Abolmaesumi

Guided Random Forests for Identification of Key Fetal Anatomy andImage Categorization in Ultrasound Scans . . . . . . . . . . . . . . . . . . . . . . . . . . 687

Mohammad Yaqub, Brenda Kelly, A.T. Papageorghiou,and J. Alison Noble

Contents – Part III XLVII

Dempster-Shafer Theory Based Feature Selection with SparseConstraint for Outcome Prediction in Cancer Therapy . . . . . . . . . . . . . . . . 695

Chunfeng Lian, Su Ruan, Thierry Denœux, Hua Li, and Pierre Vera

Disjunctive Normal Shape and Appearance Priors with Applications toImage Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703

Fitsum Mesadi, Mujdat Cetin, and Tolga Tasdizen

Positive Delta Detection for Alpha Shape Segmentation of 3DUltrasound Images of Pathologic Kidneys . . . . . . . . . . . . . . . . . . . . . . . . . . . 711

Juan J. Cerrolaza, Christopher Meyer, James Jago, Craig Peters,and Marius George Linguraru

Non-local Atlas-Guided Multi-channel Forest Learning for HumanBrain Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719

Guangkai Ma, Yaozong Gao, Guorong Wu, Ligang Wu,and Dinggang Shen

Model Criticism for Regression on the Grassmannian . . . . . . . . . . . . . . . . . 727Yi Hong, Roland Kwitt, and Marc Niethammer

Structural Edge Detection for Cardiovascular Modeling . . . . . . . . . . . . . . . 735Jameson Merkow, Zhuowen Tu, David Kriegman,and Alison Marsden

Sample Size Estimation for Outlier Detection . . . . . . . . . . . . . . . . . . . . . . . . 743Timothy Gebhard, Inga Koerte, and Sylvain Bouix

Multi-scale Heat Kernel Based Volumetric Morphology Signature . . . . . . 751Gang Wang and Yalin Wang

Structural Brain Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760Muhammad Razib, Zhong-Lin Lu, and Wei Zeng

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769