Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors
Name
nihms-637925.pdf
Description
Accepted version
Size
458.88 KB
Format
Adobe PDF
Checksum (MD5)
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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.
Date Issued
2014
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-319-10470-6_96
