A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model. (June 2021)

• Record Type: Journal Article
• Authors: Gegundez-Arias, Manuel E.; Marin-Santos, Diego; Perez-Borrero, Isaac; Vasallo-Vazquez, Manuel J.
• Is Part Of: Computer methods and programs in biomedicine. Volume 205(2021)
• Publication Date: 2021-06
• Subjects: Deep learning -- Convolutional neural networks -- Blood vessel -- Segmentation -- Fundus images; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106081
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A deep learning-based vessel segmentation method in fundus images is presented. It uses a convolutional neural network based on a UNet model simplified version. The method is evaluated on DRIVE, STARE and CHASE_Db1 retinal image databases. It was capable of working with the highest level of accuracy and robustness. The method reaches better performance than the rest of state-of-art methods. Abstract: Background and Objective: Automatic monitoring of retinal blood vessels proves very useful for the clinical assessment of ocular vascular anomalies or retinopathies. This paper presents an efficient and accurate deep learning-based method for vessel segmentation in eye fundus images. Methods: The approach consists of a convolutional neural network based on a simplified version of the U-Net architecture that combines residual blocks and batch normalization in the up- and downscaling phases. The network receives patches extracted from the original image as input and is trained with a novel loss function that considers the distance of each pixel to the vascular tree. At its output, it generates the probability of each pixel of the input patch belonging to the vascular structure. The application of the network to the patches in which a retinal image can be divided allows obtaining the pixel-wise probability map of the complete image. This probability map is then binarized with a certain threshold to generate the blood vessel segmentation provided by the method. Results: The method has been developed and evaluated in the DRIVE, STARE and CHASE_Db1 databases, which offer a manual segmentation of the vascular tree by each of its images. Using this set of images as ground truth, the accuracy of the vessel segmentations obtained for an operating point proposal (established by a single threshold value for each database) was quantified. The overall performance was measured using the area of its receiver operating characteristic curve. The method demonstrated robustness in the face of the variability of the fundus images of diverse origin, being capable of working with the highest level of accuracy in the entire set of possible points of operation, compared to those provided by the most accurate methods found in literature. Conclusions: The analysis of results concludes that the proposed method reaches better performance than the rest of state-of-art methods and can be considered the most promising for integration into a real tool for vascular structure segmentation.