An adaptive fusion of RGB channel features U-Net for retinal vessel segmentation

Mingyuan Xi; Zikang Gong

doi:10.1117/12.3035315

19 July 2024 An adaptive fusion of RGB channel features U-Net for retinal vessel segmentation

Mingyuan Xi, Zikang Gong

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131O (2024) https://doi.org/10.1117/12.3035315
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Accurate segmentation of retinal vessels in fundus color photos holds significant importance in diagnosing ophthalmic conditions. This process plays a crucial role in identifying and analyzing various eye diseases. However, the retinal vascular structure is highly complex, with imbalances in multiscale and foreground-background proportions, making automatic segmentation challenging. Studies have indicated that valuable information is contained in the three RGB channels of color retinal vessel images. Nevertheless, there has been no research to date on integrating the information from the RGB channels with U-shaped segmentation networks for training. Therefore, in this paper, we propose an Attention U-Net with adaptive fusion of RGB three-channel features. The selective channel attention fusion module can adaptively integrate RGB three-channel features, and the fused features are input into the U-shaped network for training. In addition, an Attention Gate module is added before each upsampling concatenation operation in the decoder stage of the U-Net network. This effectively highlights specific target areas while suppressing irrelevant regions of the input image. Experimental results demonstrate that our method outperforms the segmentation results of other algorithms in three datasets in recent years.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingyuan Xi and Zikang Gong "An adaptive fusion of RGB channel features U-Net for retinal vessel segmentation", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131O (19 July 2024); https://doi.org/10.1117/12.3035315

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KEYWORDS

Image segmentation

RGB color model

Education and training

Feature fusion

Image fusion

Surgery

Silver

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