Projects:ExpectationMaximizationSegmentation

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Bayesian Segmentation of MRI Images

Segmentation algorithms based on the Expectation Maximization (EM) theory have proved themselves capable of results of an exceptional quality. Generally such results were obtained by carefully optimizing the parameters for a specific MRI protocol and a specific anatomical region. Besides the segmentation of a standard size MRI scan often requires a processing time in the order of minutes or hours. Because of these contraints, EM algorithms have found a limited usability in the clinical environment. Our project aims at addressing these issues and designing a new framework that would be easily trackable by a clinician. The background of our team encompasses Computer Science and Radiology, as well as Research and Industry. Our focus will be threefold, first to identify the bottlenecks of existing EM algorithms, second to validate the quality of our method on a collection of real life scans, and finally to provide an implementation intuitive enough that it could be accepted in the hospital and therefore make a difference for the treatment of the patient.

Key Investigators

• Sylvain Jaume, MIT
• Koen Van Leemput, MGH
• Polina Golland, MIT
• Ron Kikinis, BWH
• Steve Pieper, BWH