Rethinking automatic segmentation of gross target volume from a decoupling perspective

Jun ShiZhaohui WangShulan RuanMinfan ZhaoComputerized Medical ImagingGraphics (CMIG‘24) (SCI Q1IF=5.7

医学影像与智能诊断方向论文：Rethinking automatic segmentation of gross target volume from a decoupli

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内容导读Reading note

分类与摘要Category & abstract

聚焦医学影像分割、诊断或数据选择，体现 AI 方法在临床相关任务中的应用。

Evidence note

a, Ziqi Zhua, Hongyu Kana, Hong Ana,b,∗, Xudong Xue c, Bing Yand a School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China b Laoshan Laboratory Qingdao, Qindao, 266221, China c Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, China d Department of radiation oncology, The First Affiliated Hospital of US || a, Ziqi Zhua, Hongyu Kana, Hong Ana,b,∗, Xudong Xue c, Bing Yand a School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China b Laoshan Laboratory Qingdao, Qindao, 266221, China c Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, China d Department of radiation oncology, The First Affiliated Hos || ral Universities, China (GrantsNo. YD2150002001), National Key Research and Development Program of China, China (GrantsNo. 2016YFB1000403) and Laoshan Laboratory, China (GrantsNo. LSKJ202300305). References Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D., 2018a. DRINet for medical image segmentation. IEEE Trans. Med. Imaging 37 (11), 2453–2462, URL: https://doi.org/10.1109/tmi.2018.2835 || ral Universities, China (GrantsNo. YD2150002001), National Key Research and Development Program of China, China (GrantsNo. 2016YFB1000403) and Laoshan Laboratory, China (GrantsNo. LSKJ202300305). References Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D., 2018a. DRINet for medical image segmentation. IEEE Trans. Med. Imaging 37 (11), 2453–2462, URL: https://doi.org/10.1109/tmi.

Metadata

完整论文信息Full record

title: Rethinking automatic segmentation of gross target volume from a decoupling perspective
title_zh: 待补充
abstract: 待补充
abstract_zh: 待补充
keywords: Medical, 学位认定 A
year: 0
published_date: 待补充
online_date: 待补充
paper_type: Journal
publication_status: Published
volume: from
issue: 待补充
pages: 待补充
article_number: 待补充
publisher: 待补充
doi: 10.1016/j.compmedimag.2023.102323
research_area: 医学影像与智能诊断
tags: Medical, 学位认定 A
category: 医学影像与智能诊断
summary: 聚焦医学影像分割、诊断或数据选择，体现 AI 方法在临床相关任务中的应用。
authors: Jun Shi, Zhaohui Wang, Shulan Ruan, Minfan Zhao, Computerized Medical Imaging, Graphics (CMIG‘24) (SCI Q1, IF=5.7
corresponding_authors: 待补充
affiliations: 崂山实验室, 待补充
funding: 崂山实验室项目

Research area

医学影像与智能诊断

Citation

Jun Shi, Zhaohui Wang, Shulan Ruan, Minfan Zhao, et al. Rethinking Automatic Segmentation of Gross Target Volume from a Decoupling Perspective, Computerized Medical Imaging and Graphics (CMIG‘24) (SCI Q1，IF=5.7)

BibTeX

@article{acsand_35,
  title = {Rethinking automatic segmentation of gross target volume from a decoupling perspective},
  year = {},
  doi = {10.1016/j.compmedimag.2023.102323}
}