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

doi:10.1016/j.ijmedinf.2019.03.015

	BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation
	Guo, Song 1; Wang, Kai 1,2; Kang, Hong 1,3; Zhang, Yujun 4; Gao, Yingqi 1; Li, Tao 1
	2019-06-01
发表期刊	INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
ISSN	1386-5056
卷号	126 页码:105-113
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.
关键词	Vessel segmentation Fundus image Deep supervision Short connection
DOI	10.1016/j.ijmedinf.2019.03.015
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Health Care Sciences & Services ; Medical Informatics
WOS记录号	WOS:000465414600013
出版者	ELSEVIER IRELAND LTD
引用统计	被引频次：101[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4242
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.Nankai Univ, Tianjin, Peoples R China 2.KLMDASR, Tianjin, Peoples R China 3.Beijing Shanggong Med Technol Co Ltd, Beijing, Peoples R China 4.Chinese Acad, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Guo, Song,Wang, Kai,Kang, Hong,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	Guo, Song,Wang, Kai,Kang, Hong,Zhang, Yujun,Gao, Yingqi,&Li, Tao.(2019).BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS,126,105-113.
MLA	Guo, Song,et al."BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation".INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 126(2019):105-113.