# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 4 Experiments and Results
### Discussion  
Due to the complex morphology of retinal vessels, which includes branching, bending, and irregular shapes, and the small proportion of vessels leading to severe class imbalance, retinal vessel segmentation poses significant challenges. Additionally, retinal fundus images are often affected by noise, resulting in a low image contrast. Therefore, the task of retinal vessel segmentation is highly challenging.  
In retinal vessel segmentation tasks, false positives and false negatives can lead to different consequences. First, false positives may result in incorrect diagnoses or unnecessary treatments. If non-vessel regions are erroneously labeled as blood vessels, clinicians may mistakenly believe that abnormalities exist and proceed with unnecessary further examinations or therapies. On the other hand, false negatives may lead to overlooking lesions or abnormalities in the retina, such as vessel occlusion or abnormal vessel morphology. If crucial vessel regions are erroneously excluded, clinicians may miss important clues for diagnosing diseases, leading to delayed treatment or inadequate therapy. Therefore, reducing the occurrences of false positives and false negatives is crucial in retinal vessel segmentation tasks.  
The proposed MsFE block extracts low-level features from different scales, and the MsAF block integrates channel attention from low-level features and spatial attention from high-level features, enabling the model to comprehend the image content more comprehensively. As observed in Figures 5-7, the proposed model accurately identified small vessels in the images, demonstrating lower false negatives compared with the baseline network. Meanwhile, the analysis of the differential images in the previous section for Figures 8-10 demonstrated that the false positives and false negatives of the proposed model were lower than those of the baseline model across all three datasets. All of these indicate that the fusion of features from different scales contributed to the improvement of accuracy in retinal vessel segmentation.  
After a comprehensive analysis of the data in Tables 5-7, it is evident that in comparison with other state-of-the-art models, the proposed model achieved the highest values in sensitivity and F1-score on the three datasets. Although the proposed model did not attain the highest values in specificity, accuracy, and AUC, the differences compared with the other advanced models were minimal. Sensitivity measures a model's ability to identify true positive samples, while the F1-score provides a balanced evaluation of the model's classification performance on both positive and negative samples, which is particularly useful in scenarios with class imbalance. In the context of retinal vessel segmentation tasks, correctly identifying vessel pixels in the image is crucial. The small proportion of vessel pixels in retinal images led to a severe class imbalance issue. Therefore, given that the proposed model attained the highest values in sensitivity and F1-score on the DRIVE dataset, CHASE_DB1 dataset, and STARE dataset, it demonstrated excellent segmentation performance.  
Overall, the segmentation performance of the proposed model was satisfactory. However, there were still some segmentation errors due to the unique morphology of the vessels and factors such as a low image contrast. Additionally, the limited size of the datasets may pose challenges to the generalization capability of the model.