na-mic national alliance for medical image computing mit algorithms polina golland mit computer...
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![Page 1: NA-MIC National Alliance for Medical Image Computing MIT Algorithms Polina Golland MIT Computer Science and Artificial Intelligence Laboratory](https://reader035.vdocuments.net/reader035/viewer/2022062517/56649f0d5503460f94c21006/html5/thumbnails/1.jpg)
NA-MICNational Alliance for Medical Image Computing http://na-mic.org
MIT Algorithms
Polina GollandMIT Computer Science and Artificial
Intelligence Laboratory
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National Alliance for Medical Image Computing http://na-mic.org
Project overview
• Non-parametric segmentation– Exemplar-based priors, label fusion segmentation– Radiotherapy planning, atrial fibrillation
• Brain connectivity modeling– Joint models of anatomical and functional connectivity– Huntington’s disease
• Models of pathology evolution– Segmentation and time series modeling– Traumatic brain injury
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National Alliance for Medical Image Computing http://na-mic.org
Non-Parametric Segmentation
• Generative model for label fusion– Segmentation algorithms
• Applications: brain, left atrium of the heart
Volume Overlap
Sabuncu ‘09, ‘10; Depa ‘10
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National Alliance for Medical Image Computing http://na-mic.org
Efficient Label Fusion
• Pre-align all training images • Use one registration to align new image• Perform label fusion
Depa ‘11
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National Alliance for Medical Image Computing http://na-mic.org
Scar Localization
• Segment left atrium in the blood pool images• Register with DCE images• Use endocardium outline as a spatial prior for scar• Map onto the surface and threshold
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National Alliance for Medical Image Computing http://na-mic.org
Going Forward
• Common coordinate frame– Registration uncertainty– Towards full generative model
• Application to radiotherapy planning– Main challenge: accurate registration
• Sliding deformations– Utah 1, UNC• Allowing variable smoothness – BU• Joint registration of images and surfaces – Utah 2
– Registration in the presence of pathology and artifacts• Selecting close matches – Utah 1
– Alternative – interactive segmentation, BU
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National Alliance for Medical Image Computing http://na-mic.org
Brain Connectivity Modeling
• Joint model for anatomical and functional connectivity– Latent group connectivity template– Signal likelihood shared across subjects
• Application to population studies– Changes in the connectivity template
Control Template Disease Template
Reduced
Increased
Venkataraman ‘10, ‘12
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National Alliance for Medical Image Computing http://na-mic.org
Current and Future Directions
• A model of disease foci – Region-based model of connectivity changes– From connection-based to region-based
• Going forward– Application to a broad range of diseases, including HD
• Tractography analysis - UNC
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National Alliance for Medical Image Computing http://na-mic.org
Evolution of Pathology
• Physical model of evolution– Diffusion, proliferation
• Statistical model of imaging– Segmentations and spectroscopy
• TBI – Utah 2
• Output: model parameters and prediction
Menze ‘10, ‘11
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National Alliance for Medical Image Computing http://na-mic.org
Conclusions
• Methodological developments– Exemplar-based segmentation – Connectivity analysis– Segmentation and evolution of pathology
• DBP challenges– Registration in the presence of pathology– Connection between physiological and
neurobiological models and image analysis
• Going forward– Joint work with the DBPs