# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 3 Experiment and Results
### Comparisons
#### 3.5.2 Qualitative Analysis  
Figure 6 displays the segmentation results of U-Net, Deeplabv3+, Att U-Net, and our proposed MU-Net. Each dataset is represented by two rows of segmentation result images. In the DRIVE dataset, the first row consists of healthy fundus images, while the second row contains retinopathy images. To ensure consistency, both the U-Net and Att U-Net models were trained using the same parameter set and experimental environment.  
To observe further the segmentation results of tiny blood vessels, we opted to enlarge and focus on specific details within the overall image which is shown in Figure 7. Local areas of interest are outlined using green and red rectangles.  
Figures 6 and 7 show that compared with our proposed MU-Net, Deeplabv3+, and Att U-Net can only segment relatively large blood vessels, while tiny blood vessels are often lost at the ends or intersections of the blood vessel tree. This is evident in the bottom row of Figure 6. The Deeplabv3+ algorithm, for instance, exhibits vessel thickening, merging, or inadequate accuracy. As can be clearly seen from Figure 6, the segmentation results of Deeplabv3+ tend to contain more content, leading to a significant thickening of blood vessels. The possible reason for this is that Deeplabv3+ employs convolution with different dilation rates instead of downsampling operations. While this increases the receptive field of the network, dilation convolution involves discrete sampling on the feature map. Discrete sampling is more effective in obtaining feature information on large targets. However, for small targets, setting a high inflation rate may cause the learned features to lack correlation due to the large interval. This can result in incorrect feature information and affect the prediction of small objects, ultimately leading to unclear segmentation boundaries and judgment errors. Although the Att U-Net algorithm successfully segments microvessel ends in the STARE dataset, it shows insufficient segmentation and noticeable vessel rupture in the Drive and CHASE_DB1 datasets, respectively. Analyzing retinal blood vessels,  
particularly microvessels, is crucial for diagnosing, monitoring, and planning treatment for eye diseases like macular degeneration, diabetic retinopathy, and retinal vein occlusion. These diseases can cause retinal ischemia due to vascular occlusion, leading to retinal neovascularization. In the advanced stages of these diseases, abnormal growth of new, small blood vessels can lead to vision impairment or even blindness. Therefore, the loss of small vessels in vessel segmentation may result in missed diagnoses and delayed treatment for these diseases.  
Further comparison is made between U-Net and MU-Net, which have relatively good performance. In comparison to the U-Net baseline, MU-Net focuses more on the connectivity of microvessels, resulting in clearer and well-connected vessel outlines at the ends. However, U-Net sometimes produces discontinuous prediction results for certain microvessels. Refer to the second row of Figure 7 for retinal blood vessel images containing numerous microvessels. Our proposed MU-Net integrates multi-scale features in the encoder stage and incorporates skip connections. Furthermore, it leverages the crucial information between channels in the decoder stage to enhance the segmentation results of fine blood vessels. This method not only provides accurate segmentation results for tiny blood vessels but also preserves the spatial morphology more effectively. As a result, the  
Figure 6: Comparison of overall 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.  
approach proposed in this paper enhances the qualitative performance of segmentation by improving the segmentation quality.