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Heart Chamber Segmentation from CT Using Convolutional Neural Networks

机译：CT使用卷积神经网络的心脏室分割

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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.

机译：CT经常用于用器官的放射疗法规划，并进行诊断评估和参数优化的兴趣区域。对于心脏分割，已经提出了许多用于左心室分割的方法，但是对于整个心脏的同时分割很少。在这项工作中，我们展示了一种卷积神经网络（CNN）的基于3D CT的心脏腔室分割方法，具有5级：左心室，右心室，左心房，右心房和背景。我们实现了87.2％±3.3％的整体精度，整体室精度为85.6±6.1％。基于深度学习的分割方法可以为CT图像上的心脏分割提供自动工具。

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《SPIE Medical Imaging Conference》|2018年|v. p.|共6页
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作者
James D. Dormer; Ling Ma; Martin Halicek; Carolyn M. Reilly; Eduard Schreibmann; Baowei Fei;
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中图分类计算技术、计算机技术;
关键词
Cardiac imaging; Image segmentation. Deep Learning. Whole heart segmentation; CT imaging; Convolutional neural networks; Heart chamber segmentation;

机译：心脏成像;图像分割。深度学习。全心细分;CT成像;卷积神经网络;心室分割;

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4. Heart Chamber Segmentation from CT Using Convolutional Neural Networks [C] . James D. Dormer, Ling Ma, Martin Halicek, SPIE Medical Imaging Conference . 2018

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Heart Chamber Segmentation from CT Using Convolutional Neural Networks

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