Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors
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...
| Main Authors: | , , , , , , , , , , |
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| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
Springer International Publishing
2020
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| Online Access: | https://hdl.handle.net/1721.1/126637 |
| _version_ | 1826196574496096256 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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 |
| author_sort | Dalca, Adrian Vasile |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:29:16Z |
| format | Book |
| id | mit-1721.1/126637 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:29:16Z |
| publishDate | 2020 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1266372022-09-27T09:46:52Z Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors 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 Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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. NIH (Grants 1K25EB013649-01, NAC-P41EB015902, U54-EB005149, NS082285, K23NS064052 and U01NS069208) 2020-08-17T21:19:51Z 2020-08-17T21:19:51Z 2014 2014-09 2019-05-29T17:47:45Z Book http://purl.org/eprint/type/ConferencePaper 9783319104690 9783319104706 0302-9743 1611-3349 https://hdl.handle.net/1721.1/126637 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 en http://dx.doi.org/10.1007/978-3-319-10470-6_96 International Conference on Medical Image Computing and Computer-Assisted Intervention Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer International Publishing PMC |
| spellingShingle | 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 Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors |
| title | Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors |
| title_full | Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors |
| title_fullStr | Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors |
| title_full_unstemmed | Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors |
| title_short | Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors |
| title_sort | segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors |
| url | https://hdl.handle.net/1721.1/126637 |
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