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Proceedings Paper

Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images
Author(s): Thomas P. O'Donnell; Ning Xu; Randolph M. Setser; Richard D. White
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Paper Abstract

Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.

Paper Details

Date Published: 2 May 2003
PDF: 10 pages
Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.480422
Show Author Affiliations
Thomas P. O'Donnell, Siemens Corporate Research, Inc. (United States)
Ning Xu, Univ. of Illinois/Urbana-Champaign (United States)
Randolph M. Setser, Cleveland Clinic Foundation (United States)
Richard D. White, Cleveland Clinic Foundation (United States)

Published in SPIE Proceedings Vol. 5031:
Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications
Anne V. Clough; Amir A. Amini, Editor(s)

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