Heart Chamber Segmentation from CT Using Convolutional Neural Networks

• Type of Material: Article
• 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
• Language: English
• Date: 2018-01-01
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 SPIE.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2293554
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 10578
• Grant/Funding Information: This research is supported in part by NIH grants (CA176684, CA156775, and CA204254) and by the National Cancer Institute (NCI) via NRG Oncology, a member of the NCI National Clinical Trials Network with Federal funds from the Department of Health and Human Services under Grant Number U10 CA37422.
• Keywords: Engineering; Deep Learning; DISEASE; Technology; Whole heart segmentation; Science & Technology; MODELS; Cardiac imaging; Image segmentation; Optics; Physical Sciences; ATLAS; Heart chamber segmentation; CT imaging; Engineering, Biomedical; Convolutional neural networks
• Research Categories: Engineering, Biomedical

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.