SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vess
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ORIGINAL PAPER
SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation Tiejun Yang 1,2 & Tingting Wu 3 & Lei Li 3 & Chunhua Zhu 3
# Society for Imaging Informatics in Medicine 2020
Abstract Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately. Keywords Retinal vessel segmentation . Generative adversarial network . Short connection block . Dense block
Introduction The eyes are one of the most important sensory organs of the human body, but many people in the world are suffering from blindness. Among many eye diseases that cause blindness, fundus diseases such as senile macular degeneration, diabetic retinopathy, and hypertensive retinopathy are the main causes of blindness. The study and the analysis of retinal vessel geometric characteristics such as vessel diameter, branch angles,
and branch lengths have become the basis of medical applications related to early diagnosis and effective monitoring of retinal pathology [1]. In practical clinical diagnosis, ophthalmologists mainly perform manual segmentation of retinal vascular images based on their professional knowledge and personal experience. However, due to the imbalance of the number of doctors and patients, the number of medical images has been increasing in recent years, which makes manual segmentation time-
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10278-020-00339-9) contains supplementary material, which is available to authorized users. * Tingting Wu [email protected]
1
Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, ZhengZhou 450001, China
Tiejun Yang [email protected]
2
School of Artificial Intelligence and Big Data, Henan University of Technology, ZhengZhou 450001, China
Lei Li [email protected]
3
College of Information Science
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