Rectum Segmentation in Brachytherapy Dataset Using Recurrent Network

Kai Hsiang Lin, Jui Hung Chang, Ti Hao Wang, Hoe Yuan Ong, Pau Choo Chung

研究成果: Conference contribution

摘要

In brachytherapy, the segmentation accuracy of the target tumor and surrounding organs is very important. In recent years, deep learning models have improved the performance of organ segmentation and have been widely used. However, it is still a huge challenge for some organs with variable shapes. The basic idea of the work presented in this paper is to accurately divide the rectal computed tomography image dataset so that patients can obtain more accurate brachytherapy. In this work, we used 3D U-Net and Long ShortTerm Memory (LSTM) to improve the accuracy of rectal segmentation. This model was trained and tested on the rectal computed tomography image dataset, which contains 51 patients undergoing radiation therapy. The dice coefficient is used as the evaluation index in all results of organ segmentation. After experiments are done, it can be seen that the proposed method has good performance in rectal segmentation.

原文English
主出版物標題Proceedings - 2020 International Computer Symposium, ICS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面232-236
頁數5
ISBN(電子)9781728192550
DOIs
出版狀態Published - 2020 12月
事件2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
持續時間: 2020 12月 172020 12月 19

出版系列

名字Proceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference2020 International Computer Symposium, ICS 2020
國家/地區Taiwan
城市Tainan
期間20-12-1720-12-19

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 電腦科學應用
  • 資訊系統
  • 資訊系統與管理
  • 計算數學

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