1. NA-MIC National Alliance for Medical Image Computing Analysis and Results of Brockton VA study: Controls vs Schizophrenics Personality Disorder Martin Styner, Ipek Oguz, UNC Jim Levitt, Martha Shenton, B&W, Brockton VA/Harvard`

2. National Alliance for Medical Image Computing http://na-mic.org TOC NAMIC Data: SPD study Caudate shape analysis –Steps of Shape Pipeline (simplified) –Shape Analysis Results Global & Local –Parcellation Corpus Callosum segmentation and subdivision

3. National Alliance for Medical Image Computing http://na-mic.org Data SPD study Data through NAMIC Manual segmentations of the caudate (with existing subdivision)

4. National Alliance for Medical Image Computing http://na-mic.org Shape Analysis Workflow Caudate (Fusion) Segmentation Preprocessing & Parameterization SPHARM- PDM Shape QC Shape & Corresp. Alignment & Scale Feature Computation e.g. Subdivision or Mean Shape Difference QC of Features & Statistical Results Statistical Analysis Of Features

5. National Alliance for Medical Image Computing http://na-mic.org Pre-processing Sample case Segmentation after pre-processing Hole filling 6 connect Mean-curvature smoothing 0.5mm^3 resolution

6. National Alliance for Medical Image Computing http://na-mic.org SPHARM Shape Spherical parameterization Mirroring of right caudate Alignment to first order ellipsoid Alignment to template

7. National Alliance for Medical Image Computing http://na-mic.org SPHARM Shape Overlay of voxel segmentation (red) with SPHARM (blue) Average Error ~ 0.11mm

8. National Alliance for Medical Image Computing http://na-mic.org SPHARM correspondence

9. National Alliance for Medical Image Computing http://na-mic.org SPHARM Mean Caudate Left MeanRight Mean Cnt: Transparent blue, PSD: solid red, ICV scale Difference between means

10. National Alliance for Medical Image Computing http://na-mic.org Statistical Testing Global Shape Difference –No Scale: Left: p = 0.13, Right: p = 0.016 –ICV Scale: Left: p = 0.46, Right: p = 0.062 Local Significance (NoScale), Mean Diff, T 2 latmedRRLL

11. National Alliance for Medical Image Computing http://na-mic.org Caudate Subdivision Skeleton based Subdivision (11 parts) Fusion into 4 parts (ant/post head, body, tail)

12. National Alliance for Medical Image Computing http://na-mic.org Caudate Subdivision Subdivision Automatic QC images

13. National Alliance for Medical Image Computing http://na-mic.org Caudate Subdivision L0.080.130.120.070.040.060.050.010.10 R0.140.180.030.200.060.010.020.060.01 L0.030.05 0.01 R0.040.080.010.03 ICV Scale Orig Scale same pattern all significant

14. National Alliance for Medical Image Computing http://na-mic.org Caudate subdivision Good agreement with local shape analysis L0.030.05 0.01 R0.040.080.010.03 lateral medial R R L L

15. National Alliance for Medical Image Computing http://na-mic.org Caudate analysis Prior work: –No diff. ant L and R, ICV + original –Diff. posterior region on R but not L UNC subdivision –All subparts different in original scale –Ant L and R different in ICV scale –Diff posterior region on L and R Explanation: –Variability cuts/intraventricular foramen –Region of cut shows difference in R in UNC subdivision –Small number of subjects

16. National Alliance for Medical Image Computing http://na-mic.org Conclusion Caudate Study Shape shows local and global differences on R, but only local on L Subdivisions and local shape agree quite well Both local shape analysis and subdivision suggest main effect in caudate head and some effect in parts of the tail

17. National Alliance for Medical Image Computing http://na-mic.org Corpus Callosum Methods & reliability Results How about other Brockton VA datasets? General Witelson Scheme Mostly manual Can be automized

18. National Alliance for Medical Image Computing http://na-mic.org CC segmentation Automatic model based seg. Start from average Failure rate < 1% / 3% In case of failure, manual correction of parameters

19. National Alliance for Medical Image Computing http://na-mic.org Stable also with Anomalies

20. National Alliance for Medical Image Computing http://na-mic.org CC subdivision DTI fiber tracking based Corpus Callosum subdivision method –MICCAI 2005 Ant-frontal Post-frontal Parietal Occ+temp

21. National Alliance for Medical Image Computing http://na-mic.org Probabilistic Subdivision Not hard boundaries 5 training datasets: Average model Applicable on retrospective data Average Probabilistic Model

22. National Alliance for Medical Image Computing http://na-mic.org Example Pediatric Growth Regional Corpus Callosum growth in 4 pediatric cases age 2y to age 4y

23. National Alliance for Medical Image Computing http://na-mic.org CC reliability 100% reproducible segmentation, subdivision 1 subject, 5 sites with 2 scans within 24h T1, T2, PD: 1.5 mm & 3mm slice Single rater (where manual) Interp. to 1mm 3, registration to ACPC atlas –3 channel T1, T2, PD EMS for WM –T1 EMS for WM –Manual using T1 EMS –Manual model based (no parameter knowledge) –Pure manual (IRIS)

24. National Alliance for Medical Image Computing http://na-mic.org EMS 3 Segmentation

25. National Alliance for Medical Image Computing http://na-mic.org CC reliability Model manual better than EMS1 manual –Model manual performed after EMS1: bias Pure manual worst, only single case(better) EMS3 shows best performance Subdivision performs in the range of CC segmentation Area measurements differ between methods

26. National Alliance for Medical Image Computing http://na-mic.org CC Analysis in SPD data No p-vals below 0.4 No differences at all! Is this negative finding expected? What have others found in this population? How about the other datasets in NAMIC- VA: –Chronic schizophrenia (2 sets) –First episodes (2 sets) –We had findings before in other studies (autism, fragile X)

27. National Alliance for Medical Image Computing http://na-mic.org

28. ITK InsightSNAP A level set semi- automatic segmentation tool Visualization tool Postprocessing tool 5 year history Here: use for caudate segmentation Webpage with tools Brief Demo of SNAP –Bools & 3D cuts

29. National Alliance for Medical Image Computing http://na-mic.org Caudate Segmentation Pediatric (2y & 4y) autism, fragile X, developmentally delayed and controls Show document and figures Protocol online available: http://www.psychiatry.unc.edu/autismresearch/mri/r oiprotocols.htm

30. National Alliance for Medical Image Computing http://na-mic.org Other SNAP Projects Segmentation of lateral ventricles using probability maps from tissue segmentation –ICC > 0.99, time ~ 10 min per case Segmentation of mandible/maxilla for pre/post surgical evaluation from CB-CT

31. National Alliance for Medical Image Computing http://na-mic.org The end (for now…)