期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 39, 期 5, 页码 1392-1403出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2950051
关键词
Feature extraction; Retinal vessels; Image segmentation; Diseases; Blood vessels; Biomedical imaging; Vessel segmentation; encoder-decoder; deep learning; regularized walk; vessel reconnection
类别
资金
- National Natural Science Foundation of China [61773297, 61601029]
- Open Fund of Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System [2016ZNSS01A]
- Zhejiang Provincial Natural Science Foundation [LZ19F010001]
- Grant of Ningbo 3315 Innovation Team
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.
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