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Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors

Author(s)
Dalca, Adrian Vasile; Sridharan, Ramesh; Cloonan, Lisa; Fitzpatrick, Kaitlin M.; Kanakis, Allison; Furie, Karen L.; Rosand, Jonathan; Wu, Ona; Sabuncu, Mert; Rost, Natalia S.; Golland, Polina; ... Show more Show less
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
We propose and demonstrate an inference algorithm for the automatic segmentation of cerebrovascular pathologies in clinical MR images of the brain. Identifying and differentiating pathologies is important for understanding the underlying mechanisms and clinical outcomes of cerebral ischemia. Manual delineation of separate pathologies is infeasible in large studies of stroke that include thousands of patients. Unlike normal brain tissues and structures, the location and shape of the lesions vary across patients, presenting serious challenges for prior-driven segmentation. Our generative model captures spatial patterns and intensity properties associated with different cerebrovascular pathologies in stroke patients. We demonstrate the resulting segmentation algorithm on clinical images of a stroke patient cohort.
Date issued
2014
URI
https://hdl.handle.net/1721.1/126637
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher
Springer International Publishing
Citation
Dalca, Adrian Vasile. et al. "Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors." International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2014, Springer International Publishing, 2014. © 2014 Springer International Publishing
Version: Author's final manuscript
ISBN
9783319104690
9783319104706
ISSN
0302-9743
1611-3349

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