Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.

Journal: BioMed research international
Published Date: Feb 28, 2024

Abstract

PURPOSE: Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation.

Authors

  • Yufeng Ye
    Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
  • Naiwen Zhang
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
  • Dasheng Wu
    Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Xun Cai
    Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
  • Xiaolei Ruan
    Jiuquan Satellite Launch Center, Lanzhou, Gansu, People's Republic of China.
  • Liangliang Chen
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Kun Huang
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. Kun.Huang@osumc.edu.
  • Zi-Ping Li
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Po-Man Wu
    Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong.
  • Jinzhao Jiang
    Department of Radiology, Shenzhen University General Hospital, Shenzhen, China.
  • Guo Dan
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China. danguo@szu.edu.cn.
  • Zhenpeng Peng
    Department of Radiology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

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