# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 4 Experiments and Results
### Results  
#### 4.3.1 Comparison with Baseline Models  
We conducted experiments on several high-performing segmentation models, including FCN [31], DeepLab v3+ [32], U-net [26], and Unet++ [33], and used the results of these models as baselines. The dice score, IoU score, ROC-AUC score, and AUPR score of nmODE-Unet and the baseline models on the IDRiD dataset are summarized in Tables 1 and 2. The ROC curves and the PR curves are shown in Figures 3 and 4, respectively. In addition, the binary segmentation images on the IDRiD dataset are visualized in Figure 5.  
As observed from Tables 1 and 2 and Figure 5, FCN excels in recognizing EX and SE lesions, but its segmentation results for HEs and MAs are not satisfactory. DeepLab v3+ excels in recognizing EXs and SEs, but performs poorly in its segmentation results for MAs. Unet++ demonstrates a strong overall performance in recognizing SE, HE, and MA lesions. Unet performs well in recognizing EX and HE lesions, but its performance in recognizing SE is notably much lower compared to other baseline models. Overall, the baseline models are effective in identifying larger lesions, but encounter difficulties in segmenting very small lesions like MAs. Compared to the baseline models, the nmODE-Unet not only performs remarkably in identifying larger lesions, but also significantly improves the segmentation performance for smaller lesions like MAs. In terms of evaluation metrics, nmODE-Unet achieved higher dice scores by 7.45%, 11.20%, 1.41%, and 1.75%, higher IoU scores by 5.79%, 3.95%, 2.33%, and 1.52%, and higher AUPR scores by 6.67%, 6.02%, 5.23%, and 8.10% in the segmentation of EXs, SEs, HEs, and MAs, respectively, when compared to the second-place model. Regarding ROC-AUC scores, it obtained higher scores by 1.51%, 0.06%, and 4.47% in the segmentation of EXs, SEs, and MAs, respectively. Although the ROC-AUC score of the U-net model in HE segmentation is higher than that of nmODE-Unet by 0.55%, it is evident that its performance is notably insufficient in comparison to nmODE-Unet across other indicators. Figure 4: (**a-d**): The PR curves for EXs, SEs, HEs, and MAs on the IDRiD dataset. Figure 3: (**a–d**): The ROC curves for EXs, SEs, HEs, and MAs on the IDRiD dataset. From Figure 5, it can be observed that, for the segmentation of EXs, the lesions within the red box in the ground truth have a large area and appear as blocks. The baseline model exhibits poor segmentation in this area, identifying lesions as dots and showing a higher rate of false negatives. For the segmentation of HEs, the lesions within the green box in the ground truth consist of only three patches. However, all models segment more lesions in this area, indicating a noticeable presence of false positives. Overall, the segmentation performance of nmODE-Unet is significantly superior to that of the baseline model. We also conducted experiments on the e-ophtha dataset. The Dice score, IoU score, ROC-AUC score, and AUPR score of nmODE-Unet and the baseline models on the e-ophtha dataset are summarized in Tables 3 and 4. The ROC curves and the PR curves on the e-ophtha dataset are shown in Figures 6 and 7, respectively. From Tables 3 and 4, as well as Figures 6 and 7, it can be observed that nmODE-Unet outperforms the baseline models in  
Figure 5: The binary segmentation results of four kinds of lesions. The first row shows the color fundus images, the second row shows the ground truth. The text on the left side from the third to the seventh row indicates the models used, and the corresponding segmentation results of each model are shown on the right side. all evaluation metrics. Furthermore, when comparing the segmentation results with those from the IDRiD dataset, it becomes evident that all methods show a significant decrease in segmentation performance on the e-ophtha dataset. However, the extent of the decline in segmentation results for nmODE-Unet is notably lower than that of the baseline models, particularly in the case of MA lesions.