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


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)


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


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


Label Fusion Segmentation

Test Image

Subject Specific Label Prior

New Segmentation

PairwiseRegistration

Training Data


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


Left Atrium Segmentation

• More accurate than baseline methods• Correctly identified all veins• Local prior for scar location

Weighted fusionMajorityManual Parametric

Depa, MICCAI Workshop 2010


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


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


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

NA-MIC National Alliance for Medical Image Computing Statistical Models of Anatomy and Pathology...

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