# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 4 Discussion  
Retinal blood vessel segmentation is a complex task due to the inconsistent shape and size of blood vessels at different levels of image granularity, as well as their tendency to bend and intersect in various configurations. Conventional machine learning approaches often struggle to accurately detect and segment these blood vessels, particularly microvessels, leading to limited segmentation accuracy and sensitivity. Consequently, enhancing the algorithm's sensitivity to identify microvessels holds significant importance in facilitating doctors' diagnosis.  
The experiments revealed that the baseline model has limitations in accurately extracting microvessels and tends to break at the ends of blood vessels, resulting in low sensitivity. To address these issues, this study proposes the use of a multi-scale residual convolution module in the encoder stage to enhance blood vessel features. Additionally, selective nuclear units are implemented at skip junctions to localize receptive fields that match the size of retinal vessels. Furthermore, a residual attention module is designed in the decoder stage to reduce noise interference and remove redundant information. The MU-Net efficiently segments microvessels and maintains proper connectivity at the end of blood vessels.  
However, this study still has limitations and requires further optimization of the retinal microvessel segmentation model. On the one hand, we will continue to enhance the contrast between blood vessels and background by optimizing the preprocessing steps, which will ultimately improve the segmentation accuracy. At the same time, we have drawn inspiration from [PERSON] et al. [55], who proposed a dual-threshold iterative algorithm to extract weak vascular pixels and enhance vascular connectivity. This work has provided  
Figure 7: Comparison of local segmentation results: (**a**) original images, (**b**) ground truth, (**c**) U-Net segmentation results [13], (**d**) Deeplabv3+ segmentation results [14], (**e**) Att U-Net segmentation results [27], and (**f**) MU-Net segmentation results. Note: Local areas of interest are outlined using green and red rectangles.  
valuable insights for our future endeavors. Additionally, we plan to employ post-processing techniques to enhance the visual effects of the segmented image. On the other hand, we plan to incorporate multiple skip connections from Unet++ [56] to effectively fuse feature information at different scales and stages. This will enable more precise segmentation of microvessel structures. In our future work, the sensitivity of microvessel segmentation and good connectivity of blood vessel ends need to be further enhanced.  
Conceptualization, X.H. and T.W.; Methodology, X.H. and T.W.; writing--original draft, X.H.; writing--review and editing, T.W.; Supervision, T.W. and W.Y.; Funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Nos. 62276061 and 62006041.  
The data presented in this study is available on request from the corresponding authors, and the dataset was jointly completed by the team, so the data is not publicly available.  
The authors declare no conflict of interest.