# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 5 Conclusions  
In this paper, we propose a novel model for retinal vessel segmentation built upon the U-Net backbone architecture. For each layer of the U-Net backbone, two additional blocks, namely, the MsFE block and the MsAF block, were incorporated between the encoder and decoder. The MsFE block extracts low-level features from different scales, while the MsAF block performs attention fusion across various scales. Experimental evaluations were conducted on the DRIVE dataset, CHASE_DB1 dataset, and STARE dataset, demonstrating that MsAF-UNet exhibited competitive performance. In future work, our focus will be on investigating multi-scale attention fusion mechanisms to further enhance the segmentation performance of retinal vessels. **Author Contributions:** Conceptualization, S.W., Y.C. and Z.Y. ; methodology, S.W., Y.C. and Z.Y. ; software, S.W., Y.C. and Z.Y. ; validation, S.W. ; formal analysis, S.W., Y.C. and Z.Y. ; investigation, S.W., Y.C. and Z.Y. ; resources, Y.C. and Z.Y. ; data curation, S.W. ; writing--original draft, S.W. ; writing--review and editing, Y.C. and Z.Y. ; visualization, S.W. ; supervision, Y.C. and Z.Y. ; project administration, Y.C. and Z.Y. ; funding acquisition, Y.C. and Z.Y. All authors read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant no. 61702349) and National Major Science and Technology Projects of China (grant no. 2018 AAA0100201). Not applicable. The data that support the findings of this study are openly available in the DRIVE dataset at [[https://drive.grand-challenge.org/](https://drive.grand-challenge.org/)]([https://drive.grand-challenge.org/](https://drive.grand-challenge.org/)) accessed on 28 February 2024, the CHASE_DB1 dataset at [[https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1/accessed](https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1/accessed)]([https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1/accessed](https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1/accessed)) on 28 February 2024, and the STARE dataset at [[https://cccas.clemson.edu/~ahoover/stare/probing/index.html](https://cccas.clemson.edu/~ahoover/stare/probing/index.html)]([https://cccas.clemson.edu/~ahoover/stare/probing/index.html](https://cccas.clemson.edu/~ahoover/stare/probing/index.html)) accessed on 28 February 2024. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Psanet: Point-wise spatial attention network for scene parsing. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8-14 September 2018; pp. 267-283. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15-20 June 2019; pp. 3146-3154. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Self-attention generative adversarial networks. In Proceedings of the International Conference on Machine Learning. PMLR, Long Beach, CA, USA, 9-15 June 2019; pp. 7354-7363. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Expectation-maximization attention networks for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October-2 November 2019; pp. 9167-9176. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. _Neurocomputing_**2019**, _365_, 31-43.