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A Complete System for Automatic Extraction of Left Ventricular Myocardium from CT Images using Shape Segmentation and Contour Evolution

Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Image Process
Volume Number:
Issue Number:
IEEE Trans Image Process. 2014 Mar;23(3):1340-51.
PubMed ID:
Myocardium segmentation, left ventricle, shape segmentation, contour evolution
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 HL085417/HL/NHLBI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Zhu L., Gao Y., Appia V., Yezzi A., Arepalli C., Faber T., Stillman A., Tannenbaum A. A Complete System for Automatic Extraction of Left Ventricular Myocardium from CT Images using Shape Segmentation and Contour Evolution. IEEE Trans Image Process. 2014 Mar;23(3):1340-51. PMID: 24723531. PMCID: PMC4133272.
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The left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation. In particular, the blood pool of a CT image is extracted and represented as a triangulated surface. Then, the left ventricle is localized as a salient component on this surface using geometric and anatomical characteristics. After that, the myocardial surfaces are initialized from the localization result and evolved by applying forces from the image intensities with a constraint based on the initial myocardial surface locations. The proposed framework has been validated on 34-human and 12-pig CT images, and the robustness and accuracy are demonstrated.