# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 5 Conclusions  
In this paper, we proposed a novel framework named nmODE-Unet, which is based on the nmODE block and U-net backbone for the segmentation of DR lesions. The performance of nmODE-Unet was evaluated on the IDRiD dataset, the e-ophtha dataset, and the LGG segmentation dataset. nmODE-Unet exhibited a superior performance over the baseline model across all datasets. On the IDRiD dataset, nmODE-Unet demonstrated a competitive performance compared to state-of-the-art methods and the top ten teams in the IDRiD challenge.  
While the nmODE block has demonstrated an excellent performance, its adaptability to a wider range of medical image types remains to be verified. MedSAM [36] exhibits a robust performance across a diverse array of datasets, proving that a single foundation model can handle various segmentation tasks, eliminating the need for task-specific models.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **EX** & **SE** & **HE** & **MA** & **mAUTR** \\ \hline L-seg [18] & 0.7495 & 0.7113 & 0.6374 & 0.4627 & 0.6515 \\ M2 MRF [20] & 0.8216 & 0.6932 & 0.6869 & 0.4880 & 0.6724 \\ SAA [34] & 0.8812 & 0.7281 & 0.6704 & 0.4152 & 0.6738 \\ CARNet [35] & 0.8675 & 0.7125 & 0.6389 & 0.5148 & 0.6834 \\ nmODE-Unet & 0.8361 & 0.7381 & 0.6262 & 0.5177 & 0.6795 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with other state-of-the-art methods.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **FLOPs (G)** & **Parameters (M)** & **(g)/Batch** & **Memory (M)** \\ \hline FCN [31] & 156.18 & 18.64 & 0.01 & 298.07 \\ DeepLab v3+ [32] & 136.21 & 59.34 & 0.05 & 953.50 \\ U-net [26] & 181.35 & 19.20 & 0.01 & 304.74 \\ Unet+ [33] & 213.78 & 9.16 & 0.02 & 153.98 \\ nmODE-Unet & 181.35 & 19.20 & 1.13 & 485.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational efficiency and resource requirements.  
Integrating the nmODE block into more deep learning networks to build foundation models, similar to MedSAM and capable of handling diverse segmentation tasks, is a topic worthy of further research.  
Our research goal is to apply nmODE-Unet to clinical diagnosis; we believe that, by training nmODE-Unet with a more diverse set of medical images representing various types, it has tremendous potential to become a valuable tool for clinical diagnostics.  
Conceptualization, S.W., Y.C. and Z.Y.; Methodology, S.W., Y.C. and Z.Y.; Software, S.W., Y.C. and Z.Y.; Validation, S.W.; Formal analysis, S.W., Y.C. and Z.Y.; Investigation, S.W., Y.C. and Z.Y.; Resources, Y.C. and Z.Y.; Data curation, S.W.; Writing--original draft, S.W.; Writing--review & editing, Y.C. and Z.Y.; Visualization, S.W.; Supervision, Y.C. and Z.Y.; Project administration, Y.C. and Z.Y.; Funding acquisition, Y.C. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61702349) and National Major Science and Technology Projects of China (Grant No. 2018 AAA0100201).  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
References