Thesis Defense: Population-wise Consistent Segmentation of Diffusion Weighted Magnetic Resonance Images
Speaker: Ulas Ziyan , MIT CSAIL
Date: May 20 2008
Time: 10:00AM to 11:00AM
Location: 32-G449 (Kiva)
Contact: Ulas Ziyan, 617-258-8832, ulas@mit.edu
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In this thesis, we investigate unsupervised and semi-supervised methods to construct anatomical atlases and segment medical images. We propose an integrated registration and clustering algorithm to compute an anatomical atlas of fiber-bundles as well as deep gray matter structures from a population of diffusion tensor MR images (DT-MRI).
We refer to this algorithm as ``Consistency Clustering'' since the outputs of the algorithm include population-wise consistent segmentations and correspondence between the subjects. The consistency is ensured through using a single anatomical model for the whole population, which is similar to the atlases used by experts for manual labeling. We experiment with both parametric and non-parametric models for the gray matter and white matter segmentation problems, each model resulting in a different kind of atlas.
Consistent population-wise segmentations require development of several integrated algorithms for clustering, registration, atlas-building and outlier rejection. In this thesis we develop, implement and evaluate these tools individually and together as a population-wise segmentation tool. Together, Consistency Clustering enables automatic atlas construction in DT-MRI for a population, either normal or affected by a neural disorder. Consistency Clustering also provides the user the choice to include prior knowledge through a few labeled subjects (semi-supervised) or compute an anatomical atlas in a completely data-driven manner (unsupervised). Furthermore, resulting anatomical models are compact representations of populations and can be used for population-wise morphometry.
We implement and evaluate these methods using in vivo DT-MRI datasets. We investigate the benefits of population-wise segmentation as opposed to individually segmenting subjects, as well as effects of noise and initialization on the segmentations.
Thesis Supervisors:
Carl-Fredrik Westin
Associate Professor of Radiology at Harvard Medical School
W. Eric L. Grimson
Bernard Gordon Professor of Medical Engineering
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