Segmentation of detailed, patient-specific models from medical imagery can provide invaluable assistance for surgical planning and navigation. Current segmentation methods often make errors when confronted with subtle intensity boundaries. Adding knowledge of expected shape of a structure, and the range of normal variations in shape, can greatly improve segmentation, by guiding it towards the most likely shape consistent with the image information. The resulting segmentations can be used to plan surgical procedures, and when registered to the patient, can provide navigational guidance around critical structures. Many neurological diseases, such as Alzheimer's, schizophrenia, and Fetal Growth Restriction, affect the shape of specific anatomical areas. To understand the development and progression of these diseases, as well as to develop methods for classifying instances into diseased or normal classes, 1 needs methods that capture differences in shape distributions between populations. Our goal is to develop and validate methods for learning from images concise representations of anatomical shape and its variability, Modeling shape distributions will improve segmentation algorithms by biasing the search towards more likely shapes. It will also enable quantitative analysis based on shape in population studies, where imaging is used to study differences in anatomy between populations, as well as changes within a population, for example with age. The proposed research builds on prior methods for segmentation and shape analysis, using tools from computer vision and machine learning applied to questions of shape representation, shape based segmentation and shape analysis for population studies. We plan to further develop the methods and to validate them with our collaborators in several different applications, including surgical planning, neonatal imaging and image-based studies of aging and Alzheimer's disease.

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National Institute of Neurological Disorders and Stroke (NINDS)
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Special Emphasis Panel (ZRG1-MABS (01))
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Liu, Yuan
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Massachusetts Institute of Technology
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