# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 4 Experiment
### Comparison of Results of Different Segmentation Algorithms  
This paper compares the performance of the proposed model with other segmentation methods: the modified Chan-Vese model [43], super-pixel method [44], multi-field depth discontinuity method [45], U-Net [22], BCRF [46],multi-feature-based method [47], improved U-Net [22], CGAN [48] and deep learning-based methods [49]. Five evaluation indexes were compared. A value of \"_-_\" indicates that the results of this evaluation index were not given in the original work, as shown in Tables 7 and 8. Although a large number of studies did not calculate accuracy, specificity and sensitivity, as shown in Table 7, compared with the results given in [22; 24; 48], the proposed method achieves optimal accuracy and specificity of the OD segmentation. Table 8 also shows the comparison of [24; 49]. The method in this paper achieves the best accuracy and specificity of segmentation of the OC. The indicators calculated by a large number of works are Dice and BLE; Tables 7 and 8 show that, in these two evaluation indicators, the proposed method is superior to other OD and OC segmentation algorithms. This shows the benefit of expressing the segmentation task as a bounding box detection problem in our work. Figure 8: Comparison of segmented images of each model in the DRIVE dataset. (**a**) is original image; (**b**) is ground truth of (**a**); (**c**) MPU; (**d**) MPR; (**e**) MPAR. Figure 9: Comparison of ROC curves of the DRIVE dataset model. Figure 10 shows the effect of the video cup segmentation of the model and other methods in this paper. Compared with columns d, f, g, and h, the proposed method is more accurate at segmenting the optic disc and the boundary is more reasonable, especially compared with the Graph Cut prior method [51]. Compared with column e, the segmentation boundary of the superpixel method is a relatively regular ellipse. This method is affected by the conventional shape. The extracted boundary results are not accurate and reliable. This also proves that the model proposed in this paper can more accurately and effectively collect the boundary information of the OD and OC. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Dice** & **BLE** \\ \hline Vessel Bend [43] & - & - & - & 0.9600/0.0200 & 8.930/2.960 \\ Superpixel [44] & - & - & - & 0.9500/0.0200 & 9.380/5.750 \\ Multiview [45] & - & - & - & 0.9600/0.0200 & 8.930/2.960 \\ U-Net [24] & 0.9700/ - & 0.9800/ - & 0.9600/ - & 0.9500/ - & - \\ BCRF [46] & - & - & - & 0.9700/ 0.0200 & 6.610/3.550 \\ [PERSON] et al. [47] & - & - & - & 0.9470/ - & 8.885/ - \\ [PERSON] et al. [22] & 0.9353/ - & 0.9684/ - & 0.8623/ - & 0.9500/ - & - \\ CGAN [48] & 0.9800/ - & 0.9900/ - & **0.9800**/ - & 0.9700/ - & - \\ [PERSON] et al. [49] & - & - & - & 0.9400/ - & 9.883/ - \\ MPAR (ours) & **0.9967**/**0.0022** & **0.9981**/**0.0022** & 0.9365/ 0.0659 & **0.9817**/**0.0156** & **4.765**/**2.933** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the OD results of different baseline methods on the Drishti-GS1 dataset. Figure 10: Comparison of different method model segmentation images of the Drishti-GS1 dataset. (**a**) is the original image; (**b**) is the ground truth; (**c**) is the result of the method in this paper; (**d**) BCRF [46]; (**e**) Superpixel [44]; (**f**) Multiview [45]; (**g**) Graph Cut prior [51]; (**h**) Vessel bend [43].