the pet/ct working group: 2013-2014. ct segmentation challenge informatics issues multi-site...
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The PET/CT Working Group: 2013-2014
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CT Segmentation Challenge Informatics IssuesMulti-site algorithm comparison
Task: CT-based lung nodule segmentationEvaluate algorithm performance
Bias, repeatability of volumesOverlap measuresUnderstand sources of variability
• 52 nodules from 5 collections hosted in The Cancer Imaging Archive (TCIA)
• LIDC (10)• RIDER (10)• CUMC Phantom (12)• Stanford (10) • Moffitt (10)
Data
Participants and Algorithms
• CUMC: marker-controlled watershed and geometric active contours
• Moffitt Cancer Center: multiple seed points with region growing. Ensemble segmentation obtained from the multiple grown regions.
• Stanford University: 2.5 dimension region growing using adaptive thresholds initialized with statistics from a “seed circle” on a representative portion of the tumor
Future Plans
Informatics
PET Segmentation Challenge
• Phase II: Hardware phantom scanned at 2+ sites (UI, UW)
• NEMA IEC Body Phantom Set™• Model PET/IEC-BODY/P • Four Image Sets per Site• Generate accurate volumetric
segmentations of the objects in the phantom scans
• Calculate the following indices for each of the objects: VOI volume, Max, PEAK & AVERAGE Concentration, Metabolic Tumor Volume
•Phase III: PET challenge with clinical data (H&N)
Four phase challenge starting with software phantom (DRO), hardware phantom scanned at multiple sites, segmenting clinical data and correlating PET with outcomes
Phase I: DRO
Future Plans
Results
• Catalog of CT segmentation tools• Feature extraction project: Assess impact of segmentations on
features (shape, texture, intensity) implemented at different QIN sites
• Created converters for a range of data formats• Used TaCTICS to compute metrics• Agreed to use DICOM-SEG or DICOM-RT for future
segmentation challenges• Exploring use of NCIPHUB for future challenges
• Digital Reference Object: Generated by UW/QIBA
• 7 QIN sites participated• UW, Moffitt, Iowa, Stanford,
Pittsburgh, CUMC, MSKCC• Software packages used included PMOD, Mirada
Medical RTx, OSF tool, RT_Image, CuFusion, 3D Slicer
• After some effort, all sites were able to calculate the DRO SUV metrics correctly
Nodules volume by collection. Most nodules in the LIDC and phantom collection were small while others had a wide range of sizes
All pairwise dice coefficients (all runs, all algorithms by nodule) by collection indicates shows better agreement between algorithms on the phantom nodules (CUMC) than on clinical data (ANOVA, p<0.05)
Intra-algorithm agreement was much higher than inter-algorithm agreement (p <0.05)
Exploring causes of variability
Some nodules (e.g., Lg) have high variability (typically heterogeneous)
Estimated volume by algorithm for highlighted
nodule
Bias in estimating volume (phantom data, 12 nodules)
Each site submitted 3-4 segmentations for each of the 52 nodules allowing the assessment of intra- and inter-algorithm agreement
Bias (estimated-true volume) for CUMC-phantom nodules shows a difference between algorithms (ANOVA with blocking, p <<0.05) Reproducibility of algorithms
Examining bias for large and small nodules
Statistically significant difference in intra-
algorithm Dice coefficients
Volume by lesion as measured by repeat runs of
algorithms
Dice coefficient (all algorithms, all runs) of
nodules in Stanford collection (ordered by volume left to right)
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