Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net.

Journal: Journal of applied clinical medical physics
Published Date: Aug 3, 2021

Abstract

PURPOSE: Manual delineation of a rectal tumor on a volumetric image is time-consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2-weighted images, but automatic segmentation on diffusion-weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U-shaped neural network (U-Net) is proposed to automatically segment rectal tumors on diffusion-weighted images.

Authors

  • Hai-Tao Zhu
    Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Xiao-Yan Zhang
    Department of Dermatology, Department of Ultrasound, General Hospital of Beijing Military Command, Beijing, China.
  • Yan-Jie Shi
    Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China.
  • Xiao-Ting Li
  • Ying-Shi Sun

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