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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS051826-04
Application #
7351765
Study Section
Special Emphasis Panel (ZRG1-MABS (01))
Program Officer
Liu, Yuan
Project Start
2005-02-15
Project End
2010-01-31
Budget Start
2008-02-01
Budget End
2009-01-31
Support Year
4
Fiscal Year
2008
Total Cost
$281,588
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
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