Rectum Segmentation in Brachytherapy Dataset Using Recurrent Network

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 International Computer Symposium, ICS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages232-236
Number of pages5
ISBN (Electronic)9781728192550
DOIs
Publication statusPublished - 2020 Dec
Event2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
Duration: 2020 Dec 172020 Dec 19

Publication series

NameProceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference2020 International Computer Symposium, ICS 2020
Country/TerritoryTaiwan
CityTainan
Period20-12-1720-12-19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Computational Mathematics

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