7 March 2007 Automatic 4D segmentation of the left ventricle in cardiac-CT-data
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Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65123N (2007); doi: 10.1117/12.707626
Event: Medical Imaging, 2007, San Diego, CA, United States
Abstract
The manual segmentation and analysis of 4D high resolution multi slice cardiac CT datasets is both labor intensive and time consuming. Therefore, it is necessary to supply the cardiologist with powerful software tools, to segment the myocardium and the cardiac cavities in all cardiac phases and to compute the relevant diagnostic parameters. In recent years there have been several publications concerning the segmentation and analysis of the left ventricle (LV) and myocardium for a single phase or for the diagnostically most relevant phases, the enddiastole (ED) and the endsystole (ES). However, for a complete diagnosis and especially of wall motion abnormalities, it is necessary to analyze not only the motion endpoints ED and ES, but also all phases in-between. In this paper a novel approach for the 4D segmentation of the left ventricle in cardiac-CT-data is presented. The segmentation of the 4D data is divided into a first part, which segments the motion endpoints of the cardiac cycle ED and ES and a second part, which segments all phases in-between. The first part is based on a bi-temporal statistical shape model of the left ventricle. The second part uses a novel approach based on the individual volume curve for the interpolation between ED and ES and afterwards an active contour algorithm for the final segmentation. The volume curve based interpolation step allows the constraint of the subsequent segmentation of the phases between ED and ES to very small search-intervals, hence makes the segmentation process faster and more robust.
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Dominik Fritz, Julia Kroll, Rüdiger Dillmann, Michael Scheuering, "Automatic 4D segmentation of the left ventricle in cardiac-CT-data", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65123N (7 March 2007); doi: 10.1117/12.707626; https://doi.org/10.1117/12.707626
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Image segmentation

Motion models

Statistical modeling

Principal component analysis

Data modeling

Blood

Diagnostics

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