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

  • Adrian Vasile Dalca
  • Ramesh Sridharan
  • Lisa Cloonan
  • Kaitlin M. Fitzpatrick
  • Allison Kanakis
  • Karen L. Furie
  • Jonathan Rosand
  • Ona Wu
  • Mert Sabuncu
  • Natalia S. Rost
  • Polina Golland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

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.

Keywords

Stroke Patient Markov Random Field Automatic Segmentation Multiple Sclerosis Lesion Inference Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Adrian Vasile Dalca
    • 1
  • Ramesh Sridharan
    • 1
  • Lisa Cloonan
    • 2
  • Kaitlin M. Fitzpatrick
    • 2
  • Allison Kanakis
    • 2
  • Karen L. Furie
    • 3
  • Jonathan Rosand
    • 2
  • Ona Wu
    • 2
  • Mert Sabuncu
    • 4
  • Natalia S. Rost
    • 2
  • Polina Golland
    • 1
  1. 1.Computer Science and Artificial Intelligence LabMITUSA
  2. 2.Department of Neurology, Massachusetts General HospitalHarvard Medical SchoolUSA
  3. 3.Department of Neurology, Rhode Island HospitalAlpert Medical SchoolUSA
  4. 4.Martinos Center for Biomedical ImagingHarvard Medical SchoolUSA

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