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http://dx.doi.org/10.9708/jksci.2021.26.04.029

SKU-Net: Improved U-Net using Selective Kernel Convolution for Retinal Vessel Segmentation  

Hwang, Dong-Hwan (Dept. of Computer Science and Engineering, Kangwon National University)
Moon, Gwi-Seong (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Yoon (Dept. of Computer Science and Engineering, Kangwon National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.4, 2021 , pp. 29-37 More about this Journal
Abstract
In this paper, we propose a deep learning-based retinal vessel segmentation model for handling multi-scale information of fundus images. we integrate the selective kernel convolution into U-Net-based convolutional neural network. The proposed model extracts and segment features information with various shapes and sizes of retinal blood vessels, which is important information for diagnosing eye-related diseases from fundus images. The proposed model consists of standard convolutions and selective kernel convolutions. While the standard convolutional layer extracts information through the same size kernel size, The selective kernel convolution extracts information from branches with various kernel sizes and combines them by adaptively adjusting them through split-attention. To evaluate the performance of the proposed model, we used the DRIVE and CHASE DB1 datasets and the proposed model showed F1 score of 82.91% and 81.71% on both datasets respectively, confirming that the proposed model is effective in segmenting retinal blood vessels.
Keywords
Deep Learning; Retinal Vessel Segmentation; Convolutional Neural Network; Selective Kernel Convolution; U-Net;
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