Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

Journal: Medical physics
PMID: 39072765

Abstract

BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive.

Authors

  • Feng Wen
    Department of Nephrology, Renal Research Institute, Hunan Key Lab of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University Changsha 410011, Hunan, China.
  • Jie Zhou
    Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
  • Zhebin Chen
    Chengdu Institute of Compute Application, Chinese Academy of Sciences, Chengdu, China.
  • Meng Dou
    Philips Research, Eindhoven, the Netherlands.
  • Yu Yao
    Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, ‡School of Computer Science and Technology, and §Center of Information Support & Assurance Technology, Anhui University , Hefei, 230601 Anhui, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Feng Xu
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Yali Shen
    Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University, Chengdu, China.

Keywords

External Resources

Popular Topics
Recent Journals