NA-MIC National Alliance for Medical Image Computing Statistical Models of Anatomy and Pathology...
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NA-MIC National Alliance for Medical Image Computing http://na-mic.org Statistical Models of Anatomy and Pathology Polina Golland
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National Alliance for Medical Image Computing 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
Transcript of NA-MIC National Alliance for Medical Image Computing Statistical Models of Anatomy and Pathology...
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
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
……
? nnL
ILILpL ,|,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
Mdepa, 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
