RAFF-UNet: an improved U-Net architecture for semantic segmentation of retinal vascular image

Qingshan Ye; Yong Bai; Lu Chen; Mei Wu

doi:10.1117/12.2640873

2 December 2022 RAFF-UNet: an improved U-Net architecture for semantic segmentation of retinal vascular image

Qingshan Ye, Yong Bai, Lu Chen, Mei Wu

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122880M (2022) https://doi.org/10.1117/12.2640873
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

The problem of poor segmentation of diabetic retinal vascular images has always existed. In this paper, we propose an improved U-Net architecture RAFF-UNet to address it. In the U-Net encoding module, we innovatively use RAFF attention module before down-sampling. Based on the retinal vascular segmentation dataset made by Baidu PaddlePaddle AI STUDIO, the experimental results show that compared with U-Net and Attention-UNet, the MIoU index of RAFFUNet is increased by 0.94% and 1.33% respectively, and Acc index is improved.

Citation Download Citation

Qingshan Ye, Yong Bai, Lu Chen, and Mei Wu "RAFF-UNet: an improved U-Net architecture for semantic segmentation of retinal vascular image", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122880M (2 December 2022); https://doi.org/10.1117/12.2640873

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available