A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation

Since the invention of diffusion magnetic resonance imaging (dMRI), currently the only established method for studying white matter connectivity in a clinical environment, there has been a great deal of interest in the effects of various pathologies on the connectivity of the brain. As methods for i...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2007-11, Vol.26 (11), p.1555-1561
Main Authors: Clayden, J.D., Storkey, A.J., Bastin, M.E.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biomedical imaging
Biomedical informatics
Brain
Brain - anatomy & histology
Brain modeling
Computer Simulation
Data Interpretation, Statistical
diffusion
Diffusion Magnetic Resonance Imaging - methods
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image reconstruction
Image segmentation
Imaging, Three-Dimensional - methods
In vivo
Magnetic resonance imaging
Models, Neurological
Models, Statistical
Nerve Fibers, Myelinated - ultrastructure
Pathology
Pattern Recognition, Automated - methods
probabilistic
Reproducibility of Results
segmentation
Sensitivity and Specificity
tractography
white matter
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Summary:Since the invention of diffusion magnetic resonance imaging (dMRI), currently the only established method for studying white matter connectivity in a clinical environment, there has been a great deal of interest in the effects of various pathologies on the connectivity of the brain. As methods for in vivo tractography have been developed, it has become possible to track and segment specific white matter structures of interest for particular study. However, the consistency and reproducibility of tractography-based segmentation remain limited, and attempts to improve them have thus far typically involved the imposition of strong constraints on the tract reconstruction process itself. In this work we take a different approach, developing a formal probabilistic model for the relationships between comparable tracts in different scans, and then using it to choose a tract, a posteriori, which best matches a predefined reference tract for the structure of interest. We demonstrate that this method is able to significantly improve segmentation consistency without directly constraining the tractography algorithm.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2007.905826