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Statistical Models of Anatomy and Pathology. Polina Golland. Statistical Models of Anatomy. Applications Spatial priors for segmentation Population studies Traditional approach Align images to a common template Compute mean and co-variation Challenges - PowerPoint PPT Presentation
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NA-MICNational Alliance for Medical Image Computing http://na-mic.org
Statistical Models of Anatomy and Pathology
Polina Golland
National Alliance for Medical Image Computing http://na-mic.org
Statistical Models of Anatomy
• Applications – Spatial priors for segmentation– Population studies
• Traditional approach– Align images to a common template– Compute mean and co-variation
• Challenges– Spatial variability in the structure of interest– Loss of detail– Heterogeneous populations
National Alliance for Medical Image Computing http://na-mic.org
Our Solutions
• Use training data in novel ways– handle spatial variability
• TBI, tumors– avoid the loss of detail
• Atrial Fibrillation, Huntington’s, Alzheimer’s
• Model heterogeneous populations– capture broader variability
• Atrial fibrillation, radiation therapy, Alzheimer’s
• Related topics– Registration (Guido Gerig)– Interactive segmentation (Allen Tannenbaum)
National Alliance for Medical Image Computing http://na-mic.org
Spatial Priors and Pathology
• Augmented generative model– Atlas: spatial prior for healthy tissues– Estimate: spatial prior for tumor
• Output– Common healthy tissue segmentation– Modality-specific tumor segmentation
Menze, MICCAI 2010
National Alliance for Medical Image Computing http://na-mic.org
Spatial Priors and Pathology (cont’d)
• More accurate than EM-segmentation with outlier detection
• Comparable to within-rater variability
• Going forward: TBI
Menze, MICCAI 2010
National Alliance for Medical Image Computing http://na-mic.org
Label Fusion Segmentation
Test Image
Subject Specific Label Prior
New Segmentation
PairwiseRegistration
Training Data
National Alliance for Medical Image Computing http://na-mic.org
Generative Model for Label Fusion
{Ln} {In}
L(x) I(x)
M
Test image
Training images
……
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nnLILILpL ,|,maxargˆ
Sabuncu, TMI 2010
National Alliance for Medical Image Computing http://na-mic.org
Left Atrium Segmentation
• More accurate than baseline methods• Correctly identified all veins• Local prior for scar location
Weighted fusionMajorityManual Parametric
Depa, MICCAI Workshop 2010
National Alliance for Medical Image Computing http://na-mic.org
Modeling Heterogeneous Populations
• Manifold of anatomical images– Spectral embedding– Statistical model in new space– Gerber, MedIA 2010
• Collection of sub-populations – Mixture model– Templates represent population– Sabuncu TMI 2009
noise
National Alliance for Medical Image Computing http://na-mic.org
Applications for Spatial Priors
• Identify relevant “neighborhood” for the new image– A (small) set of training examples– A (local) atlas template
• Construct patient-specific spatial prior– Average or use label fusion
• Challenges:– Reduce the number of pairwise registration steps– Model influence of selected neighborhood on new image
National Alliance for Medical Image Computing http://na-mic.org
Conclusions
• Clear need for new methods– Handle spatial variability of pathology– Handle anatomical variability in a population
• Preliminary results: local models– In the image coordinates– In the space of images
• Going forward– Development in the context of the DBPs
