Work

Heart chamber segmentation from CT using convolutional neural networks

Year: 2018

Type: conference-paper

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.

Source: SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging

DOI: 10.1117/12.2293554

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

Institutions: Emory University, Georgia Institute of Technology, Winship Cancer Institute, Augusta University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute