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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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 39; H. 5; S. 1392 - 1403
Hauptverfasser:	Mou, Lei, Chen, Li, Cheng, Jun, Gu, Zaiwang, Zhao, Yitian, Liu, Jiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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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.
AbstractList	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. 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.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.
Author	Mou, Lei Chen, Li Liu, Jiang Cheng, Jun Gu, Zaiwang Zhao, Yitian
Author_xml	– sequence: 1 givenname: Lei surname: Mou fullname: Mou, Lei email: moulei@nimte.ac.cn organization: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China – sequence: 2 givenname: Li surname: Chen fullname: Chen, Li email: chenli@wust.edu.cn organization: Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, China – sequence: 3 givenname: Jun orcidid: 0000-0003-1786-6188 surname: Cheng fullname: Cheng, Jun email: sam.j.cheng@gmail.com organization: Chinese Academy of Sciences, Cixi Institute of Biomedical Engineering, Ningbo, China – sequence: 4 givenname: Zaiwang orcidid: 0000-0001-8764-0622 surname: Gu fullname: Gu, Zaiwang email: guzaiwang@gmail.com organization: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China – sequence: 5 givenname: Yitian orcidid: 0000-0003-4357-4592 surname: Zhao fullname: Zhao, Yitian email: yitian.zhao@nimte.ac.cn organization: Chinese Academy of Sciences, Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Ningbo, China – sequence: 6 givenname: Jiang orcidid: 0000-0001-6281-6505 surname: Liu fullname: Liu, Jiang email: liuj@sustech.edu.cn organization: Chinese Academy of Sciences, Cixi Institute of Biomedical Engineering, Ningbo, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31675323$$D View this record in MEDLINE/PubMed
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Snippet	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...
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SubjectTerms	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
Title	Dense Dilated Network With Probability Regularized Walk for Vessel Detection
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