Imaging segmentation mechanism for rectal tumors using improved U-Net.

Journal: BMC medical imaging
PMID: 38654162

Abstract

OBJECTIVE: In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic segmentation of rectal tumors from an MRI is a great challenge. There are two main shortcomings in existing deep learning-based methods that lead to incorrect segmentation: 1) there are many organs surrounding the rectum, and the shape of some organs is similar to that of rectal tumors; 2) high-level features extracted by conventional neural networks often do not contain enough high-resolution information. Therefore, an improved U-Net segmentation network based on attention mechanisms is proposed to replace the traditional U-Net network.

Authors

  • Kenan Zhang
    College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
  • Xiaotang Yang
    Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China. yangxt210@126.com.
  • Yanfen Cui
    Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
  • Jumin Zhao
    College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Dengao Li
    Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, Shanxi, China.

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