MFCTrans-net: a multi-scale fusion and channel transformer net for 
retinal vessel segmentation

Zhuo Li; Biyuan Li; Jun Zhang; Jianqiang Mei

doi:10.1117/12.2682910

27 June 2023 MFCTrans-net: a multi-scale fusion and channel transformer net for retinal vessel segmentation

Zhuo Li, Biyuan Li, Jun Zhang, Jianqiang Mei

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051N (2023) https://doi.org/10.1117/12.2682910
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Automatic segmentation of retinal blood vessels is a nontrivial task due to the complexity of retinal fundus image. In this paper, a new network named MFCTrans-net is proposed for retinal blood vessel segmentation. The MFCTrans-net is an improvement over original U-Net, which can be summarized as (1) to better fuse the encoder and decoder features and reduce the semantic gap, we replace the skip connection in the original U-net by the Channel-wise Cross Fusion Transformer(CCT); (2) two side paths are added to the U-Net which allow the network to capture features at multiple scales; (3) a novel loss function is also proposed which focuses on the topology integrity of vessel meanwhile maintaining pixel segmentation accuracy. The proposed network has been developed and evaluated in the DRIVE, CHASE-DB1 and IOSTAR datasets, which offer a manual segmentation of the vascular tree by each of its images. The performance of our method is evaluated in terms of visual effects and quantitative evaluation metrics on these four publicly available datasets with comparison to several representative methods. Furthermore, we use the proposed method to segment a collection of the experimentally obtained retinal blood vessel images with poor quality. The experimental results demonstrate the performance of our proposed method.

Citation Download Citation

Zhuo Li, Biyuan Li, Jun Zhang, and Jianqiang Mei "MFCTrans-net: a multi-scale fusion and channel transformer net for retinal vessel segmentation", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051N (27 June 2023); https://doi.org/10.1117/12.2682910

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KEYWORDS

Image segmentation

Blood vessels

Transformers

Lithium

Convolution

Feature fusion

Visualization

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