关注- lstm融合U-Net结构在CT图像器官分割中的应用

Pin-Hsiu Chen, Cheng-Hsien Huang, S. Hung, Liang-Cheng Chen, Hui-Ling Hsieh, W. Chiou, Moon-Sing Lee, Hon-Yi Lin, Wei-Min Liu
{"title":"关注- lstm融合U-Net结构在CT图像器官分割中的应用","authors":"Pin-Hsiu Chen, Cheng-Hsien Huang, S. Hung, Liang-Cheng Chen, Hui-Ling Hsieh, W. Chiou, Moon-Sing Lee, Hon-Yi Lin, Wei-Min Liu","doi":"10.1109/IS3C50286.2020.00085","DOIUrl":null,"url":null,"abstract":"During the treatment planning stage of the radiotherapy, the medical physicists or doctors have to delineate the contour of the tumor and the organs at risk in order to accurately deliver enough radiation energy to the tumor and reduce such exposure to the surrounding normal tissues. Organ contouring is a time-consuming and laborious task. An automatic contouring tool is definitely required to fulfill the needs of the increasing cancer population. In this work, we proposed a fusion model that combines the network characteristics of a sequential model (sensor3D) and Attention U-Net. In which the convolutional LSTM layer is applied to study the spatial correlation between different layers in a CT image data set. The attention mechanism suppresses the irrelevant features from the complex image content and focuses on the useful messages of target organs. The clinical data contained CT image series of 108 patients and was acquired from a local hospital with IRB approval. The proposed model segmented five types of organs, lung, liver, stomach, esophagus, and heart. The segmentation accuracy rates of Dice Similarity Coefficient (DSC) were 99.27%, 95.48%, 88.19%, 80.81%, and 93.8%, respectively. We further developed a user interface that converts the AI-generated results into DICOM-RT format. Therefore the radiologists can fine-tune the results under the software used to do the routine manual-delineation tasks.","PeriodicalId":143430,"journal":{"name":"2020 International Symposium on Computer, Consumer and Control (IS3C)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Attention-LSTM Fused U-Net Architecture for Organ Segmentation in CT Images\",\"authors\":\"Pin-Hsiu Chen, Cheng-Hsien Huang, S. Hung, Liang-Cheng Chen, Hui-Ling Hsieh, W. Chiou, Moon-Sing Lee, Hon-Yi Lin, Wei-Min Liu\",\"doi\":\"10.1109/IS3C50286.2020.00085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During the treatment planning stage of the radiotherapy, the medical physicists or doctors have to delineate the contour of the tumor and the organs at risk in order to accurately deliver enough radiation energy to the tumor and reduce such exposure to the surrounding normal tissues. Organ contouring is a time-consuming and laborious task. An automatic contouring tool is definitely required to fulfill the needs of the increasing cancer population. In this work, we proposed a fusion model that combines the network characteristics of a sequential model (sensor3D) and Attention U-Net. In which the convolutional LSTM layer is applied to study the spatial correlation between different layers in a CT image data set. The attention mechanism suppresses the irrelevant features from the complex image content and focuses on the useful messages of target organs. The clinical data contained CT image series of 108 patients and was acquired from a local hospital with IRB approval. The proposed model segmented five types of organs, lung, liver, stomach, esophagus, and heart. The segmentation accuracy rates of Dice Similarity Coefficient (DSC) were 99.27%, 95.48%, 88.19%, 80.81%, and 93.8%, respectively. We further developed a user interface that converts the AI-generated results into DICOM-RT format. Therefore the radiologists can fine-tune the results under the software used to do the routine manual-delineation tasks.\",\"PeriodicalId\":143430,\"journal\":{\"name\":\"2020 International Symposium on Computer, Consumer and Control (IS3C)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Computer, Consumer and Control (IS3C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IS3C50286.2020.00085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Computer, Consumer and Control (IS3C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS3C50286.2020.00085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

在放射治疗的治疗计划阶段,医学物理学家或医生必须描绘出肿瘤和危险器官的轮廓,以便准确地向肿瘤传递足够的辐射能量,并减少对周围正常组织的照射。器官轮廓是一项费时费力的工作。为了满足不断增长的癌症人口的需求,一种自动轮廓工具是绝对需要的。在这项工作中,我们提出了一种融合了序列模型(sensor3D)和注意力U-Net的网络特征的融合模型。其中,利用卷积LSTM层研究CT图像数据集中不同层之间的空间相关性。注意机制从复杂的图像内容中抑制无关的特征,将注意力集中在目标器官的有用信息上。临床资料包括108例患者的CT图像系列,从当地一家医院获得,并经IRB批准。该模型将肺、肝、胃、食道、心脏五类器官进行了分割。Dice Similarity Coefficient (DSC)的分割准确率分别为99.27%、95.48%、88.19%、80.81%和93.8%。我们进一步开发了一个用户界面,将ai生成的结果转换为DICOM-RT格式。因此,放射科医生可以在进行常规手动描绘任务的软件下微调结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attention-LSTM Fused U-Net Architecture for Organ Segmentation in CT Images
During the treatment planning stage of the radiotherapy, the medical physicists or doctors have to delineate the contour of the tumor and the organs at risk in order to accurately deliver enough radiation energy to the tumor and reduce such exposure to the surrounding normal tissues. Organ contouring is a time-consuming and laborious task. An automatic contouring tool is definitely required to fulfill the needs of the increasing cancer population. In this work, we proposed a fusion model that combines the network characteristics of a sequential model (sensor3D) and Attention U-Net. In which the convolutional LSTM layer is applied to study the spatial correlation between different layers in a CT image data set. The attention mechanism suppresses the irrelevant features from the complex image content and focuses on the useful messages of target organs. The clinical data contained CT image series of 108 patients and was acquired from a local hospital with IRB approval. The proposed model segmented five types of organs, lung, liver, stomach, esophagus, and heart. The segmentation accuracy rates of Dice Similarity Coefficient (DSC) were 99.27%, 95.48%, 88.19%, 80.81%, and 93.8%, respectively. We further developed a user interface that converts the AI-generated results into DICOM-RT format. Therefore the radiologists can fine-tune the results under the software used to do the routine manual-delineation tasks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信