# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Experimental Results  
Automatic segmentation of skin lesions is crucial for enabling precise clinical tasks such as diagnosis, prognosis, and treatment planning. We evaluate the proposed model on the ISIC 2018 dataset to assess its effectiveness in skin lesion segmentation. As illustrated in Figure 4, HCMUNet excels at delineating lesion boundaries and effectively mitigates the edge-blurring artifacts observed in other models. The resulting segmentation masks show high fidelity to the ground truth, maintaining sharp contours and structural integrity even for lesions with irregular shapes and varying sizes. Quantitative results in Table 1 further validate the model's performance. HCMUNet achieves the best scores across all key metrics, including an mIoU of 82.19%, a DSC of 90.32%, and a Sensitivity of 91.21%, outperforming both traditional CNN-based architectures (e.g., U-Net, U-Net++, Att-UNet) and ViT-based models (e.g., TransUNet, TransFuse, Swin-UNet). Notably, it surpasses the well-established Transformer baseline, Swin-UNet, by 1.48% in mIoU, 0.66% in the DSC, and 0.90% in Sensitivity. Compared to the baseline, HCMUNet shows improved segmentation accuracy, as reflected in the higher DSC, and demonstrates greater stability across diverse cases. These consistent improvements underscore the effectiveness of HCMUNet's hybrid design, which integrates convolutional layers for detailed local feature extraction with Mampa networks to capture long-range dependencies. Overall, the results demonstrate not only strong generalization across complex scenarios but also the model's practical applicability in real-world medical image segmentation tasks. Figure 4: The figure presents segmentation results from the ISIC 2018 dataset, showcasing four representative models for comparison. In each visualization, the background is rendered in black, while the regions of interest are highlighted in white to clearly delineate the segmented structures. To further validate the generalizability of HCMUNet, we evaluate its performance on the Synapse multi-organ CT dataset, which poses challenges due to large anatomical variability and complex organ boundaries. As shown in Figure 5, our model delivers anatomically coherent segmentations, accurately capturing both the shape and spatial layout of organs, even in regions with high overlap or irregularity. Table 2 provides a comprehensive comparison with prior methods. HCMUNet achieves a DSC of 81.52 and an HD95 of 17.83, outperforming most CNNs and ViT-based counterparts. Specifically, it improves over TransUNet by 5.06% in DSC, and over Swin-UNet by 2.33%, highlighting its effectiveness in modeling both local textures and global spatial cues. In organ-wise comparisons, HCMUNet attains top scores in critical and structurally diverse organs such as the gallbladder (69.60%), right kidney (82.35%), and liver (95.10%), demonstrating high robustness across categories with varying shapes, sizes, and boundary clarity. While EMCAD performs competitively in certain cases, HCMUNet exhibits more consistent accuracy across all organs. These improvements can be attributed to its hybrid design: Convolutional encoders preserve fine-grained local patterns, while the Mamba blocks enhance global reasoning. Importantly, the reduced HD95 suggests stronger alignment with true organ contours, further reinforcing HCMUNet's suitability for complex 3D medical image segmentation tasks. As part of a broader evaluation, we applied our model to the ACDC dataset, which contains cine cardiac MRI scans annotated for three key anatomical regions, the right ventricle (RV), myocardium (MYO), and left ventricle (LV), thereby testing its cross-domain performance. This dataset poses unique challenges due to the dynamic nature of the cardiac cycle, varying wall thickness, and frequent shape deformations. Figure 6 shows that HC-MUNet produces segmentation maps with smooth contours and strong anatomical fidelity, accurately capturing cardiac structures even under substantial shape and intensity variations.