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

Biao Zheng, Chenxiao Cai, Lei Ma

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

Abstract

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.

Original languageEnglish
Title of host publication16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages672-677
Number of pages6
ISBN (Electronic)9781728177090
DOIs
Publication statusPublished - Dec 13 2020
Externally publishedYes
Event16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020 - Virtual, Shenzhen, China
Duration: Dec 13 2020Dec 15 2020

Publication series

Name16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020

Conference

Conference16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020
CountryChina
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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