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: | , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Springer-Verlag
2015
|
| Online Access: | http://hdl.handle.net/1721.1/100234 https://orcid.org/0000-0002-8422-0136 https://orcid.org/0000-0003-2516-731X |
| _version_ | 1826212167182974976 |
|---|---|
| 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 | 2024-09-23T15:17:06Z |
| format | Article |
| id | mit-1721.1/100234 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:17:06Z |
| publishDate | 2015 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/1002342019-05-17T07:51:55Z 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 Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Dalca, Adrian Vasile Sridharan, Ramesh Golland, Polina 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. Natural Sciences and Engineering Research Council of Canada (Canada Graduate Scholarships-Doctoral) National Science Foundation (U.S.). Graduate Research Fellowship National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) 1K25EB013649-01) BrightFocus Foundation (Grant AHAF-A201233) National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/Neuroimaging Analysis Center (U.S.) P41EB015902) National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) U54-EB005149) National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) NS082285) National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) K23NS064052) National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) U01NS06920) American Stroke Association (Bugher Foundation Centers for Stroke Prevention Research) 2015-12-14T03:28:40Z 2015-12-14T03:28:40Z 2014 Article http://purl.org/eprint/type/JournalArticle 978-3-319-10469-0 978-3-319-10470-6 0302-9743 1611-3349 http://hdl.handle.net/1721.1/100234 Dalca, Adrian Vasile, Ramesh Sridharan, Lisa Cloonan, Kaitlin M. Fitzpatrick, Allison Kanakis, Karen L. Furie, Jonathan Rosand, et al. “Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors.” Lecture Notes in Computer Science (2014): 773–780. OPEN_ACCESS_POLICY https://orcid.org/0000-0002-8422-0136 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/10.1007/978-3-319-10470-6_96 Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer-Verlag 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 | http://hdl.handle.net/1721.1/100234 https://orcid.org/0000-0002-8422-0136 https://orcid.org/0000-0003-2516-731X |
| work_keys_str_mv | AT dalcaadrianvasile segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT sridharanramesh segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT cloonanlisa segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT fitzpatrickkaitlinm segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT kanakisallison segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT furiekarenl segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT rosandjonathan segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT wuona segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT sabuncumert segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT rostnatalias segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors AT gollandpolina segmentationofcerebrovascularpathologiesinstrokepatientswithspatialandshapepriors |