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CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy.
Medical Physics ( IF 3.8 ) Pub Date : 2020-07-12 , DOI: 10.1002/mp.14386
Yingzi Liu 1 , Yang Lei 1 , Yabo Fu 1 , Tonghe Wang 1 , Xiangyang Tang 2 , Xiaojun Jiang 1 , Walter J Curran 1 , Tian Liu 1 , Pretesh Patel 1 , Xiaofeng Yang 1
Affiliation  

Segmentation of organs‐at‐risk (OARs) is a weak link in radiotherapeutic treatment planning process because the manual contouring action is labor‐intensive and time‐consuming. This work aimed to develop a deep learning‐based method for rapid and accurate pancreatic multi‐organ segmentation that can expedite the treatment planning process.

中文翻译:

基于CT的多器官分割,使用3D自关注U-net网络进行胰腺放射治疗。

危险器官的分割(OARs)是放射治疗计划制定过程中的薄弱环节,因为手动轮廓勾画是费力且费时的。这项工作旨在开发一种基于深度学习的方法,以快速,准确地进行胰腺多器官分割,从而加快治疗计划的制定过程。
更新日期:2020-07-12
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