A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: Oct 27, 2023

Abstract

BACKGROUND: The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a laborious and time-intensive process. Previous deep learning-based studies have mainly been focused on automatic tumor segmentation or predicting genetic/histological features. However, few studies have specifically addressed the identification of infiltrated brain areas. To bridge this gap, we aim to develop a model that can simultaneously identify infiltrated brain areas and perform accurate segmentation of gliomas.

Authors

  • Yin Li
    Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Kaiyi Zheng
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Shuang Li
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
  • Yongju Yi
    Center for Network Information, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Yufan Ren
    Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Congyue Guo
    Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Liming Zhong
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Xinming Li
    College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Lin Yao
    School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.

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