BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.
Background and Objective: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrha...
| Publicado en: | International Journal of Medical Informatics Vol. 126; pp. 105 - 114 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136017564&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136017564 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Jun2019 vid: 126 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 136017564 136017564 NLM31029251 136017564 10.1016/j.ijmedinf.2019.03.015 NLM31029251 136017564 ppf: 105 ppct: 9 formats: tig: atl: BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation. aug: au: Guo, Song Wang, Kai Kang, Hong Zhang, Yujun Gao, Yingqi Li, Tao affil: Nankai University, Tianjin, China sug: subj: Retina Anatomy and Histology Neural Networks (Computer) Retina Human Algorithms Validation Studies Comparative Studies Evaluation Research Multicenter Studies Psychological Tests ab: Background and Objective: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results.Methods: In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images.Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments.Conclusions: BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at: https://github.com/guomugong/BTS-DSN. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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