HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 7, 2022

Abstract

BACKGROUND AND OBJECTIVE: The segmentation and visualization of liver vessels in 3D CT images are essential for computer-aided diagnosis and preoperative planning of liver diseases. Due to the irregular structure of liver vessels and image noise, accurate extraction of liver vessels is difficult. In particular, accurate segmentation of small vessels is always a challenge, as multiple single down-sampling usually results in a loss of information.

Authors

  • Wen Hao
    Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Jun Su
    Department of Ultrasound, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, China.
  • Yuqing Song
    School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
  • Zhe Liu
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Chengjian Qiu
    School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
  • Kai Han
    Geneis Beijing Limited Company, Beijing 100102, China.

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