Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy.

Journal: BMC medical imaging
PMID: 39558240

Abstract

BACKGROUND AND PURPOSE: Tumor bed (TB) is the residual cavity of resected tumor after surgery. Delineating TB from CT is crucial in generating clinical target volume for radiotherapy. Due to multiple surgical effects and low image contrast, segmenting TB from soft tissue is challenging. In clinical practice, titanium clips were used as marks to guide the searching of TB. However, this information is limited and may cause large error. To provide more prior location information, the tumor regions on both pre-operative and post-operative CTs are both used by the deep learning model in segmenting TB from surrounding tissues.

Authors

  • Peng Huang
    College of Food Science, Sichuan Agricultural University, Ya'an 625014, China.
  • Hui Yan
    School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, China. Electronic address: yanhui@mail.njust.edu.cn.
  • Jiawen Shang
    Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Xin Xie
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.

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