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

Institution:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
17
Issue Number:
Pt 2
Pages:
773-80
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 2):773-80.
PubMed ID:
25485450
PMCID:
PMC4260817
Appears in Collections:
NAC, NA-MIC
Sponsors:
K25 EB013649/EB/NIBIB NIH HHS/United States
K23 NS064052/NS/NINDS NIH HHS/United States
R01 NS082285/NS/NINDS NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
U01 NS069208/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Dalca A., Sridharan R., Cloonan L., Fitzpatrick K.M., Kanakis A., Furie K.L., Rosand J., Wu O., Sabuncu M., Rost N.S., Golland P. Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors. Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 2):773-80. PMID: 25485450. PMCID: PMC4260817.
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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.

Additional Material
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Dalca-MICCAI2014-fig4.jpg (164.65kB)