Deep learning-based fully automated detection and segmentation of pelvic lymph nodes on diffusion-weighted images for prostate cancer: a multicenter study.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 40098034

Abstract

BACKGROUND: Accurate identification and evaluation of lymph nodes (LNs) in prostate cancer (PCa) patients is crucial for effective staging but can be time-consuming. We utilized a 3D V-Net model to improve the efficiency and accuracy of LN detection and segmentation.

Authors

  • Zhaonan Sun
    Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
  • Pengsheng Wu
    Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China.
  • Tongtong Zhao
    Department of Radiology, Fuyang Second People's Hospital, Fuyang, 236000, China.
  • Ge Gao
    School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou 213000, China.
  • Huihui Wang
    School of Mechanical Engineering & Automation, Dalian Polytechnic University, Qinggongyuan 1, Ganjingzi District, Dalian 116034, PR China; National Engineering Research Center of Seafood, Dalian Polytechnic University, Qinggongyuan 1, Ganjingzi District, Dalian 116034, PR China; Engineering Research Center of Seafood of Ministry of Education of China, Dalian 116034, PR China; Collaborative Innovation Center of Seafood Deep Processing, Dalian 116034, PR China. Electronic address: wanghh@dlpu.edu.cn.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • XiaoYing Wang

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