CS 2-Net: Deep learning segmentation of curvilinear structures in medical imaging

Lei Mou, Huazhu Fu, Yitian Zhao, YONGHUAI LIU, Jun Cheng, Yalin Zheng, Pan Su, Jianlong Yang, Li Chen, Alejandro F Frangi, Jiang Liu

Research output: Contribution to journalArticle (journal)peer-review

12 Citations (Scopus)
12 Downloads (Pure)

Abstract

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS 2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1×3 and a 3×1 convolutional kernel to capture boundary features. Besides, we extend the 2D attention mechanism to 3D to enhance the network's ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2D and 3D images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics.

Original languageEnglish
Article number101874
Pages (from-to)101874
JournalMedical Image Analysis
Volume67
Early online date21 Oct 2020
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Curvilinear structure
  • blood vessel
  • nerve fiber
  • segmentation
  • attention
  • deep neural network
  • Attention mechanism
  • Deep neural network
  • Segmentation
  • Blood vessel
  • Nerve fiber

Research Groups

  • Visual Computing Lab

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