Curv-Net: Curvilinear structure segmentation network based on selective kernel and multi-Bi-ConvLSTM.

Journal: Medical physics
Published Date: Feb 25, 2022

Abstract

PURPOSE: Accurately segmenting curvilinear structures, for example, retinal blood vessels or nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases. Recently, deep learning has become a popular technology to deal with the image segmentation task, and it has obtained remarkable achievement. However, the existing methods still have many problems when segmenting the curvilinear structures in medical images, such as losing the details of curvilinear structures, producing many false-positive segmentation results. To mitigate these problems, we propose a novel end-to-end curvilinear structure segmentation network called Curv-Net.

Authors

  • Yanlin He
    Shandong Normal University, School of Information Science and Engineering, No. 88, Wenhua East Road, Jinan, People's Republic of China.
  • Hui Sun
    Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China.
  • Yugen Yi
    School of Software, Jiangxi Normal University, Nanchang, China.
  • Wenhe Chen
    College of Information Sciences and Technology, Northeast Normal University, Changchun, China.
  • Jun Kong
    Stony Brook University, Stony Brook, NY.
  • Caixia Zheng
    College of Computer Science and Information Technology, Northeast Normal University, Changchun, China.

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