analysis and results of brockton va study: controls vs schizophrenics personality disorder

NA-MICNational 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`

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


Data SPD study

• Data through NAMIC

• Manual segmentations of the caudate (with existing subdivision)

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.


Shape Analysis Workflow

Caudate (Fusion) Segmentation

Preprocessing &Parameterization

SPHARM-PDM Shape

QCShape &Corresp.

Alignment& Scale

Feature Computatione.g. Subdivision or

Mean Shape Difference

QC of Features & Statistical Results

Statistical AnalysisOf Features


Pre-processing

• Sample case• Segmentation after

pre-processing• Hole filling• 6 connect• Mean-curvature

smoothing• 0.5mm^3

resolution




SPHARM Shape

• Spherical parameterization• Mirroring of right caudate• Alignment to first order ellipsoid• Alignment to template






SPHARM Shape



• Overlay of voxel segmentation (red) with SPHARM (blue)

• Average Error ~ 0.11mm


SPHARM correspondence


SPHARM Mean Caudate

Left Mean Right Mean



• Cnt: Transparent blue, PSD: solid red, ICV scale• Difference between means




are needed to see this picture. QuickTime™ and aTIFF (LZW) decompressor



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, T2

lat medR RL L


Caudate Subdivision

• Skeleton based Subdivision (11 parts)

• Fusion into 4 parts (ant/post head, body, tail)










Caudate Subdivision

• Subdivision Automatic QC images


Caudate Subdivision

L 0.08 0.13 0.12 0.07 0.04 0.06 0.05 0.01 0.10

R 0.14 0.18 0.03 0.20 0.06 0.01 0.02 0.06 0.01



L 0.03 0.05 0.05 0.01

R 0.04 0.08 0.01 0.03

ICV ScaleOrig Scale same pattern all significant


Caudate subdivision

• Good agreement with local shape analysis

L 0.03 0.05 0.05 0.01

R 0.04 0.08 0.01 0.03

lateral medial

R RL L


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






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


Corpus Callosum

• Methods & reliability• Results• How about other Brockton VA datasets?

• General Witelson Scheme

• Mostly manual• Can be automized


CC segmentation

QuickTime™ and a decompressor


• Automatic model based seg.

• Start from average

• Failure rate < 1% / 3%

• In case of failure, manual correction of parameters


Stable also with Anomalies


CC subdivision

• DTI fiber tracking based Corpus Callosum subdivision method– MICCAI 2005

Ant-frontal Post-frontal

Parietal Occ+temp


Probabilistic Subdivision

• Not hard boundaries

• 5 training datasets: Average model

• Applicable on retrospective data

Average Probabilistic Model


Example Pediatric Growth

• Regional Corpus Callosum growth in 4 pediatric cases age 2y to age 4y


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 1mm3, 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)


EMS 3 Segmentation


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

COV COV COV COV COVtotal ant front post front parietal occ-temp mean stdev

EMS3 2.72 3.10 4.12 2.47 2.53 511 14EMS1 3.61 5.37 5.20 2.49 2.63 534 19M EMS1 5.04 5.10 5.92 4.97 4.97 546 28M model 3.66 4.09 3.94 3.70 4.08 581 15M pure 5.66 - - - - 557 31M single 1.09


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)


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




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/

roiprotocols.htm


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


The end (for now…)

analysis and results of brockton va study: controls vs schizophrenics personality disorder

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