Dormer James D, Ma Ling, Halicek Martin, Reilly Carolyn M, Schreibmann Eduard, Fei Baowei
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Medical College of Georgia, Augusta, GA.
Proc SPIE Int Soc Opt Eng. 2018 Feb;10578. doi: 10.1117/12.2293554. Epub 2018 Mar 12.
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.
CT通常用于放射治疗计划，对感兴趣的器官和区域进行分割以进行诊断评估和参数优化。对于心脏分割，已经提出了许多用于左心室分割的方法，但同时分割整个心脏的方法却很少。在这项工作中，我们提出了一种基于卷积神经网络（CNN）的用于3D CT的心脏腔室分割方法，该方法可分为5类：左心室、右心室、左心房、右心房和背景。我们实现了87.2%±3.3%的总体准确率和85.6±6.1%的总体腔室准确率。基于深度学习的分割方法可能为CT图像上的心脏分割提供一种自动工具。
