SKU-Net: Improved U-Net using Selective Kernel Convolution for Retinal Vessel Segmentation (선택적 커널 합성곱을 이용한 U-Net 기반 망막 혈관 분할 모델)

Hwang Donghwan (황동환); Gwi-Seong Moon (문귀성); Kim, yoon (김윤)

doi:10.9708/jksci.2021.26.04.029

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

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(4), pp.29-37
DOI : 10.9708/jksci.2021.26.04.029
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : March 25, 2021
Accepted : April 14, 2021
Published : April 30, 2021

Hwang Donghwan ¹, Gwi-Seong Moon ¹, Kim, yoon ¹

¹강원대학교

Accredited

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

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

KJCKorea
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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.