Dense Dilated Network With Probability Regularized Walk for Vessel Detection

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,...

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Bibliographic Details
Published in:IEEE transactions on medical imaging Vol. 39; no. 5; pp. 1392 - 1403
Main Authors: Mou, Lei, Chen, Li, Cheng, Jun, Gu, Zaiwang, Zhao, Yitian, Liu, Jiang
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biomedical imaging
Blood vessels
Coders
Deep learning
Diagnosis
Diseases
encoder-decoder
Encoders-Decoders
Eye diseases
Feature extraction
Image segmentation
Medical treatment
regularized walk
Retina
Retinal vessels
vessel reconnection
Vessel segmentation
ISSN:0278-0062, 1558-254X, 1558-254X
Online Access:Get full text
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Summary: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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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2019.2950051