Automatic delineation of organs at risk in non-small cell lung cancer radiotherapy based on deep learning networks*

Author:

Yang Anning1,Lu Na2,Jiang Huayong2,Chen Diandian2,Yu Yanjun2,Wang Yadi2,Wang Qiusheng1,Zhang Fuli2

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

2. Department of Radiation Oncology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China

Abstract

Abstract Objective To introduce an end-to-end automatic segmentation method for organs at risk (OARs) in chest computed tomography (CT) images based on dense connection deep learning and to provide an accurate auto-segmentation model to reduce the workload on radiation oncologists. Methods CT images of 36 lung cancer cases were included in this study. Of these, 27 cases were randomly selected as the training set, six cases as the validation set, and nine cases as the testing set. The left and right lungs, cord, and heart were auto-segmented, and the training time was set to approximately 5 h. The testing set was evaluated using geometric metrics including the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD). Thereafter, two sets of treatment plans were optimized based on manually contoured OARs and automatically contoured OARs, respectively. Dosimetric parameters including Dmax and Vx of the OARs were obtained and compared. Results The proposed model was superior to U-Net in terms of the DSC, HD95, and ASD, although there was no significant difference in the segmentation results yielded by both networks (P > 0.05). Compared to manual segmentation, auto-segmentation significantly reduced the segmentation time by nearly 40.7% (P < 0.05). Moreover, the differences in dose-volume parameters between the two sets of plans were not statistically significant (P > 0.05). Conclusion The bilateral lung, cord, and heart could be accurately delineated using the DenseNet-based deep learning method. Thus, feature map reuse can be a novel approach to medical image auto-segmentation.

Publisher

Ovid Technologies (Wolters Kluwer Health)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3