期刊
出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2293554
关键词
Cardiac imaging; Image segmentation; Deep Learning; Whole heart segmentation; CT imaging; Convolutional neural networks; Heart chamber segmentation
资金
- NIH [CA176684, CA156775, CA204254]
- National Cancer Institute (NCI) via NRG Oncology
- Department of Health and Human Services [U10 CA37422]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据