Dense Dilated Network with Probability Regularized Walk for Vessel Detection

Lei Mou, Li Chen, Jun Cheng, Zaiwang Gu, Yitian Zhao, Jiang Liu

Research output: Journal PublicationArticlepeer-review

39 Citations (Scopus)

Abstract

The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multi-scale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method outperforms the state-of-the-art methods in accuracy, sensitivity, specificity and also area under receiver operating characteristic curve.

Original languageEnglish
Article number8886468
Pages (from-to)1392-1403
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number5
DOIs
Publication statusPublished - May 2020
Externally publishedYes

Keywords

  • Vessel segmentation
  • deep learning
  • encoder-decoder
  • regularized walk
  • vessel reconnection

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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