Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI.

Journal: Computers in biology and medicine
Published Date: Aug 15, 2023

Abstract

BACKGROUND AND OBJECTIVE: Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging.

Authors

  • Yuchun Li
    Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang City, 453003, Henan Province, People's Republic of China.
  • Yuanyuan Wu
    Department of Mathematics, Southeast University, Nanjing 210096, China; College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
  • Mengxing Huang
    State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information and Communication Engineering, Hainan University, Haikou 570288, China. Electronic address: huangmx09@hainanu.edu.cn.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Zhiming Bai
    Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou 570288, China.

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