VBNet: An end-to-end 3D neural network for vessel bifurcation point detection in mesoscopic brain images.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 2, 2021

Abstract

BACKGROUND AND OBJECTIVE: Accurate detection of vessel bifurcation points from mesoscopic whole-brain images plays an important role in reconstructing cerebrovascular networks and understanding the pathogenesis of brain diseases. Existing detection methods are either less accurate or inefficient. In this paper, we propose VBNet, an end-to-end, one-stage neural network to detect vessel bifurcation points in 3D images.

Authors

  • Yuxin Li
    University of Cincinnati, Department of Chemistry, 312 College Drive, 404 Crosley Tower, Cincinnati, Ohio 45221-0172, United States.
  • Tong Ren
    Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Junhuai Li
    Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Huaijun Wang
    Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Xiangning Li
    Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.
  • Anan Li
    Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.

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