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Automatic image‐driven segmentation of the ventricles in cardiac cine MRI
Author(s) -
Cocosco Chris A.,
Niessen Wiro J.,
Netsch Thomas,
Vonken Evertjan P.A.,
Lund Gunnar,
Stork Alexander,
Viergever Max A.
Publication year - 2008
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21451
Subject(s) - steady state free precession imaging , segmentation , computer science , artificial intelligence , left ventricles , ventricle , cardiac ventricle , computer vision , pattern recognition (psychology) , magnetic resonance imaging , medicine , radiology , cardiology
Purpose To propose and to evaluate a novel method for the automatic segmentation of the heart's two ventricles from dynamic (“cine”) short‐axis “steady state free precession” (SSFP) MR images. This segmentation task is of significant clinical importance. Previously published automated methods have various disadvantages for routine clinical use. Materials and Methods The proposed method is primarily image‐driven: it exploits the spatiotemporal information provided by modern 3D+time SSFP cardiac MRI, and makes only few and plausible assumptions about the image acquisition and about the imaged heart. Specifically, the method does not require previously trained statistical shape models or gray‐level appearance models, as often used by other methods. Results The performance of the segmentation method was demonstrated through a qualitative visual validation on 32 clinical exams: no gross failures for the left‐ventricle (right‐ventricle) on 31 (29) of the exams were found. A validation of resulting quantitative cardiac functional parameters showed good agreement with a manual quantification of 19 clinical exams. Conclusion The proposed method is feasible, fast, and robust against anatomical variability and image contrast variations. J. Magn. Reson. Imaging 2008;28:366–374. © 2008 Wiley‐Liss, Inc.