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Heart Chamber Segmentation from CT Using Convolutional Neural Networks. 2018

James D Dormer, and Ling Ma, and Martin Halicek, and Carolyn M Reilly, and Eduard Schreibmann, and Baowei Fei
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.

CT is routinely used for radiotherapy planning with organs and regions of interest being segmented for diagnostic evaluation and parameter optimization. For cardiac segmentation, many methods have been proposed for left ventricular segmentation, but few for simultaneous segmentation of the entire heart. In this work, we present a convolutional neural networks (CNN)-based cardiac chamber segmentation method for 3D CT with 5 classes: left ventricle, right ventricle, left atrium, right atrium, and background. We achieved an overall accuracy of 87.2% ± 3.3% and an overall chamber accuracy of 85.6 ± 6.1%. The deep learning based segmentation method may provide an automatic tool for cardiac segmentation on CT images.

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