Heart Chamber Segmentation from CT Using Convolutional Neural Networks

• Authors: James D. Dormer, Emory University; Ling Ma, Emory University; Martin Halicek, Medical College of Georgia; Carolyn Reilly, Emory University; Eduard Schreibmann, Emory University; Baowei Fei, Emory University
• Journal Title: Proceedings of SPIE
• Volume: Volume 10578
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE) | 2018-01-01
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1117/12.2293554
• Subjects: Engineering, Biomedical
• Keywords: Science & Technology; Technology; Physical Sciences; Engineering, Biomedical; Optics; Engineering; Cardiac imaging; Image segmentation; Deep Learning; Whole heart segmentation; CT imaging; Convolutional neural networks; Heart chamber segmentation; DISEASE; MODELS; ATLAS

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.