Work

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

Year: 2014

Type: article

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 impor-tant for understanding the underlying mechanisms and clinical outcomes of cerebral ischemia. Manual delineation of separate pathologies is infea-sible in large studies of stroke that include thousands of patients. Unlike normal brain tissues and structures, the location and shape of the le-sions vary across patients, presenting serious challenges for prior-driven segmentation. Our generative model captures spatial patterns and in-tensity properties associated with different cerebrovascular pathologies in stroke patients. We demonstrate the resulting segmentation algorithm on clinical images of a stroke patient cohort. 1

Authors: Adrian V. Dalca, Ramesh Sridharan, Lisa Cloonan, Kaitlin Fitzpatrick, Allison Kanakis, Karen L. Furie, Jonathan Rosand, Ona Wu, Mert R. Sabuncu, Natalia S. Rost, Polina Golland

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Harvard University, Massachusetts General Hospital, Rhode Island Hospital, Athinoula A. Martinos Center for Biomedical Imaging

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W2297340099

FWCI:

Cites: 14

Cited by:

Related to: 10