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BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation

文献类型：期刊论文

作者Zhang, Yujun1; Li, Tao4; Gao, Yingqi4; Kang, Hong2,4; Wang, Kai3,4; Guo, Song4
刊名INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
出版日期2019-06-01
卷号126页码:105-113
ISSN号1386-5056
关键词Vessel segmentation Fundus image Deep supervision Short connection
DOI10.1016/j.ijmedinf.2019.03.015
英文摘要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.
资助项目National Natural Science Foundation[61872200] ; National Key Research and Development Program of China[2016YFC0400709] ; National Key Research and Development Program of China[2018YFB1003405] ; Natural Science Foundation of Tianjin[18YFYZCG00060]
WOS研究方向Computer Science ; Health Care Sciences & Services ; Medical Informatics
语种英语
出版者ELSEVIER IRELAND LTD
WOS记录号WOS:000465414600013
源URL[http://119.78.100.204/handle/2XEOYT63/4241]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Tao
作者单位1.Chinese Acad, Inst Comp Technol, Beijing, Peoples R China
2.Beijing Shanggong Med Technol Co Ltd, Beijing, Peoples R China
3.KLMDASR, Tianjin, Peoples R China
4.Nankai Univ, Tianjin, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yujun,Li, Tao,Gao, Yingqi,et al. BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation[J]. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS,2019,126:105-113.
APA Zhang, Yujun,Li, Tao,Gao, Yingqi,Kang, Hong,Wang, Kai,&Guo, Song.(2019).BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS,126,105-113.
MLA Zhang, Yujun,et al."BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation".INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 126(2019):105-113.

入库方式： OAI收割

来源：计算技术研究所

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