# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Discussion  
Based on experiments conducted on three benchmark datasets, HCMUNet consistently delivers robust and high-accuracy segmentation results. However, the degree of improvement varies depending on the complexity and nature of the task. For the relatively straightforward ISIC 2018 skin lesion segmentation, HCMUNet achieves a DSC of 90.32%, outperforming U-Net by 2.9% and Swin-UNet by 0.66%, while preserving lesion boundaries with high fidelity and minimal edge blurring. Figure 7 illustrates the ROC curves of multiple models tested on the ISIC dataset. Our model (black) consistently lies closest to the top-left corner, indicating superior classification performance with a high TPR and a low FPR.  
On the more complex Synapse multi-organ segmentation task, HCMUNet reaches a DSC of 81.52%, marking a significant 5.6% gain over U-Net (75.92%), alongside a reduced HD95 of 17.83 mm, indicating strong spatial precision. It notably achieves superior accuracy on organs with diverse morphologies such as the gallbladder, right kidney, and liver, showcasing enhanced structural modeling and edge delineation capabilities. For the ACDC cardiac MRI segmentation task, HCMUNet attains an average DSC of 92.11%, outperforming advanced methods like EMCAD (91.30%) and TransCASCADE (91.59%). It achieves class-specific DSCs of 91.50% (RV) and 90.20% (MYO), reflecting its robustness across anatomical regions with varying spatial characteristics and intensity distributions. Collectively, these results demonstrate HCMUNet's strong generalization ability across both simple and highly complex segmentation scenarios. The model effectively segments target regions while preserving fine structural details, reducing both over-segmentation and under-segmentation. Furthermore, its outputs maintain sharp, continuous boundaries,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Components**} & \multicolumn{2}{c}{**Evaluation Metrics**} \\ \cline{2-6}  & **Mamba** & **Conv** & **SCDA** & **Paras (M)** & **DSC (\%)** \\ \hline \multirow{4}{*}{ISIC 2018} & ✓ & & & 44.27 & 89.67 \(\pm\) 0.68 \\  & ✓ & ✓ & & 25.43 & 90.16 \(\pm\) 0.59 \\  & ✓ & & ✓ & 55.11 & 90.03 \(\pm\) 0.73 \\  & ✓ & ✓ & ✓ & 67.17 & 85.22 \(\pm\) 1.21 \\  & ✓ & ✓ & ✓ & 36.27 & 90.32 \(\pm\) 0.53 \\ \hline \multirow{4}{*}{Synapse} & ✓ & & & 44.27 & 80.47 \(\pm\) 1.71 \\  & ✓ & ✓ & & 25.43 & 80.92 \(\pm\) 1.44 \\  & ✓ & & ✓ & 55.11 & 80.67 \(\pm\) 2.02 \\  & ✓ & ✓ & ✓ & 67.17 & 74.48 \(\pm\) 3.13 \\  & ✓ & ✓ & ✓ & 36.27 & 81.52 \(\pm\) 1.14 \\ \hline \multirow{4}{*}{ACDC} & ✓ & & & 44.27 & 90.72 \(\pm\) 0.77 \\  & ✓ & ✓ & & 25.43 & 91.34 \(\pm\) 0.55 \\ \cline{1-1}  & ✓ & & ✓ & 55.11 & 90.88 \(\pm\) 0.97 \\ \cline{1-1}  & ✓ & ✓ & ✓ & 67.17 & 87.05 \(\pm\) 1.35 \\ \cline{1-1}  & ✓ & ✓ & ✓ & 36.27 & 92.11 \(\pm\) 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effectiveness of different modules on HCMUNet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Different Data Volumes from the Dataset**} \\ \cline{2-5}  & **25\%** & **50\%** & **75\%** & **100\%** \\ \hline ISIC 2018 & 87.15 \(\pm\) 1.39 & 88.68 \(\pm\) 0.71 & 89.73 \(\pm\) 0.68 & 90.32 \(\pm\) 0.53 \\ Synapse & 75.35 \(\pm\) 3.41 & 79.46 \(\pm\) 1.85 & 81.08 \(\pm\) 1.45 & 81.52 \(\pm\) 1.14 \\ ACDC & 88.95 \(\pm\) 1.67 & 90.36 \(\pm\) 0.74 & 91.68 \(\pm\) 0.45 & 92.11 \(\pm\) 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: DSC performance (%) of HCMUNet under different training data proportions.  
minimizing blurry artifacts commonly seen in other architectures. These attributes make HCMUNet particularly suitable for real-world clinical applications involving diverse imaging modalities and anatomical targets. Figure 8 illustrates the comparison of DSC scores among various segmentation models on three benchmark datasets.