Heart chamber segmentation from CT using convolutional neural networks

James D. Dormer; Ling Ma; Martin Halicek; Carolyn M. Reilly; Eduard Schreibmann; Baowei Fei

doi:10.1117/12.2293554

12 March 2018 Heart chamber segmentation from CT using convolutional neural networks

James D. Dormer, Ling Ma, Martin Halicek, Carolyn M. Reilly, Eduard Schreibmann, Baowei Fei

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105782S (2018) https://doi.org/10.1117/12.2293554
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

Citation Download Citation

James D. Dormer, Ling Ma, Martin Halicek, Carolyn M. Reilly, Eduard Schreibmann, and Baowei Fei "Heart chamber segmentation from CT using convolutional neural networks", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105782S (12 March 2018); https://doi.org/10.1117/12.2293554

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