# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 3 Experimental Results Analysis
### Quantitative Analysis  
Figure 7 illustrates the average loss and validation mean Dice score over 200 epochs during training our model. The loss curve consistently decreases from an initial value of approximately 0.95 to a stable value near 0.75, which indicates the model's steady optimization and reduction in error. The validation Dice score begins below 0.2 and stabilizes around 0.93 in the later epochs. Our model achieved an highest dice score of 93.35 and it shows the ability of our model in segmenting the left atrium precisely. This convergence pattern highlights the model's ability to generalize well to the validation set while minimizing training loss effectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{**Baseline**} & \multicolumn{4}{c}{**Submodules**} & \multicolumn{2}{c}{**Metrics**} \\ \cline{3-8}  & & **MPCM** & **DMSA** & **AFAB** & **SFFH** & **Dice** & **Precision** & **Recall** \\ \hline \multirow{8}{*}{AMU-Net} & ✓ & - & - & - & - & 87.29 & 88.74 & 86.92 \\  & ✓ & ✓ & - & - & - & 87.96 & 88.85 & 87.47 \\  & ✓ & - & ✓ & - & - & 87.58 & 88.62 & 87.13 \\  & ✓ & - & - & ✓ & - & 87.33 & 88.39 & 86.88 \\  & ✓ & - & - & - & ✓ & 87.38 & 88.51 & 86.95 \\  & ✓ & ✓ & ✓ & - & - & 89.59 & 90.57 & 88.63 \\  & ✓ & ✓ & - & ✓ & - & 88.98 & 89.42 & 88.54 \\  & ✓ & ✓ & - & - & ✓ & 89.13 & 89.65 & 88.41 \\  & ✓ & - & ✓ & ✓ & - & 89.02 & 89.97 & 88.07 \\  & ✓ & - & ✓ & - & ✓ & 89.15 & 90.18 & 88.13 \\  & ✓ & - & - & ✓ & ✓ & 89.23 & 90.29 & 88.37 \\  & ✓ & ✓ & ✓ & ✓ & - & 91.55 & 92.31 & 90.89 \\  & ✓ & ✓ & ✓ & - & ✓ & 91.24 & 92.08 & 90.67 \\  & ✓ & ✓ & - & ✓ & ✓ & 90.07 & 90.87 & 89.53 \\  & ✓ & - & ✓ & ✓ & ✓ & 90.31 & 91.11 & 89.71 \\  & ✓ & ✓ & ✓ & ✓ & ✓ & 93.35 & 94.12 & 92.78 \\ \hline \end{tabular}
\end{table}
Table 1: Ablation study of AMU-Net to evaluate the performance of different modules.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{**L\_PL**} & \multirow{2}{*}{**L\_aux**} & **Dice** & **mIOU** & **Precision** & **Recall** \\ \hline AMU-Net & ✓ & - & 92.97 & 91.43 & 93.84 & 92.13 \\ \hline AMU-Net & ✓ & ✓ & 93.35 & 92.69 & 94.12 & 92.78 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation study of loss functions for AMU-Net.  
Table 3 presents a comparative analysis of the segmentation performance of various models based on Dice score, Precision, and Recall. SwinUNETR performs well, with a Dice score of 92.81, Precision of 93.26, and Recall of 92.14. DynUNet follows closely, achieving a Dice score of 92.50 and Precision of 93.07, indicating effective segmentation capabilities. Models like SegResNet, UNETR, and VNet show competitive performance with Dice scores ranging from 90.37 to 91.62, but they lag slightly behind in overall performance. The VarAutoEncoder model achieves a Dice score of 92.66, with relatively balanced Precision (92.14) and Recall (91.07). DDRNet-39, PIDNet-L, Mask2 Former, and SegViT also performed well by acquiring Dice scores of 90.71, 91.18, 92.88, and 92.75, respectively. Overall, AMU-Net outperforms other models in all metrics by achieving the highest Dice score of 93.35, Precision of 94.12, and Recall of 92.78. These results demonstrated the superior performance of our model in accurately capturing segmentation boundaries and minimizing false positives.