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Evaluation of ischemic stroke hybrid segmentation in a rat model of temporary middle cerebral artery occlusion using ground truth from histologic and MR data
Author(s) -
Celina Imielińska,
Yinpeng Jin,
Xin Liu,
Joel Rosiene,
Brad E. Zacharia,
Ricardo J. Komotar,
J Mocco,
Michael E. Sughrue,
Bartosz T. Grobelny,
Alex Sisti,
Josh Silverberg,
Joyce Khandji,
Hilary G. Cohen,
Sander Connolly,
Anthony D’Ambrosio
Publication year - 2005
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.596662
Subject(s) - ground truth , segmentation , computer science , artificial intelligence , stroke (engine) , occlusion , middle cerebral artery , image segmentation , pattern recognition (psychology) , computer vision , medicine , ischemia , surgery , physics , thermodynamics
A segmentation method that quantifies cerebral infarct using rat data with ischemic stroke is evaluated using ground truth from histologic and MR data. To demonstrate alternative approach to rapid quantification of cerebral infarct volumes using histologic stained slices that requires scarifying animal life, a study with MR acquire volumetric rat data is proposed where ground truth is obtained by manual delineations by experts and automated segmentation is assessed for accuracy. A framework for evaluation of segmentation is used that provides more detailed accuracy measurements than mere cerebral infarct volume. Our preliminary experiment shows that ground truth derived from MRI data is at least as good as the one obtained from the histologic slices for evaluating segmentation algorithms for accuracy. Therefore we can develop and evaluate automated segmentation methods for rapid quantification of stroke without the necessitating animal sacrifice.

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