CT images segmentation method of rectal tumor based on modified U-net

Biao Zheng, Chenxiao Cai, Lei Ma

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Computer-assisted rectal clinical diagnosis is of great significance for the early detection and treatment of rectal cancer. In data processing, it is quite challenging to achieve automatic segmentation due to the blurred boundary between lesions and healthy rectal tissue. To overcome such difficulties, we propose a rectal tumor segmentation method by using a modified U-net, thereby improving the diagnostic efficiency and accuracy. Firstly, the central coordinates of the rectal part to extract the region of interest are determined. Then, the tumor region is determined in the CT image via the YOLOv3 algorithm. Finally, the residual connection and attention mechanism are introduced to reduce the possibility of misjudgment of healthy rectal tissue as lesions to improve the accuracy of the traditional U - net model, and we use the modified U-net model to segment the rectal tumor region. The experiments show the Dice coefficient of this method can reach 83.45 %, which is about 7% higher than the traditional U-net method, and this shows the validation and merits of the proposed algorithm.

本文言語英語
ホスト出版物のタイトル16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ672-677
ページ数6
ISBN(電子版)9781728177090
DOI
出版ステータス出版済み - 12 13 2020
外部発表はい
イベント16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 - Virtual, Shenzhen, 中国
継続期間: 12 13 202012 15 2020

出版物シリーズ

名前16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020

会議

会議16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
Country中国
CityVirtual, Shenzhen
Period12/13/2012/15/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Control and Optimization

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