# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 3 Experimental Results Analysis
### Qualititative Analysis  
This study also performed qualitative analysis to evaluate the performance of our model against ground truth annotations. Figure 8 shows the model's segmentation performance on sample images. Our model demonstrates a high degree of accuracy in replicating the ground truth segmentation, as the predicted labels closely align with the annotated regions. The mask regions in the predicted labels capture the shapes, sizes, and locations of the target areas in the ground truth. This indicates that the model effectively identifies and delineates the areas of interest.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model Name** & **Dice Score** & **mIOU** & **Precision** & **Recall** &
\begin{tabular}{c} **Parameters** \\ **(M)** \\ \end{tabular} & **FLOPS (GMac)** \\ \hline SegResNet [40] & 91.25 & 90.53 & 92.48 & 90.50 & 11.76 & 196.05 \\ DynUNet [41] & 92.50 & 91.72 & 93.07 & 91.59 & 5.65 & 113.08 \\ SwinUNETR [42] & 92.81 & 91.93 & 93.26 & 92.14 & 62.19 & 328.72 \\ UNETR [43] & 90.37 & 89.15 & 91.65 & 91.18 & 87.63 & 679.24 \\ VarAutoEncoder [44] & 92.66 & 91.22 & 92.14 & 91.07 & 135.81 & 44.08 \\ VNet [45] & 91.62 & 90.87 & 92.21 & 90.94 & 45.6 & 324.16 \\ DDRNet-39 [46] & 90.71 & 90.14 & 90.97 & 91.07 & 32.38 & 140.62 \\ PIDNet-L [47] & 91.18 & 91.58 & 92.02 & 92.39 & 36.94 & 275.87 \\ Mask2 Former [48] & 92.88 & 91.42 & 93.37 & 92.55 & 215.47 & 403.31 \\ SegViT [49] & 92.75 & 91.39 & 93.22 & 91.64 & 138.24 & 432.6 \\ AMU-Net & 93.35 & 92.69 & 94.12 & 92.78 & 94.78 & 804.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of evaluation metrics for different models.  
Figure 7: Acquired loss and Dice score during the training process of AMU-Net.  
Figure 9 provides a detailed visual analysis of the segmentation performance through the visualization of corresponding false positives (FPs) and false negatives (FNs) on challenging images. The false negatives are highlighted in red, while false positives are marked in green. Across the samples, the predicted masks show a strong resemblance to the ground truth, which reflects the model's high accuracy in capturing the target regions. However, we also observed some errors at the boundaries, which reveals areas where the model missed true positives. These errors predominantly occur along the edges, which indicates the challenges in precise boundary delineation on challenging images. Nonetheless, the relatively small extent of FNs and FPs compared to the overall segmentation highlights the robustness of the model.  
Figure 8: The visualization results of AMU-net to evaluate the performance of the model.  
Figure 9: The visualization results FPs and FNs on challenging images.  
Figure 10 shows the visual comparison of segmentation results produced by our proposed AMU-Net model and four other top-performing models. The columns represent the input images, ground truth masks, predicted masks, and FN -FP areas. Our AMU-Net demonstrates superior segmentation performance which is closely aligned with the ground truth. It also shows lower FN and FP regions compared to the other models such as Var AutoEncoder, SwinUnetR, and DynUNet. This comparison highlights the effectiveness of our AMU-Net in accurately segmenting the target regions compared to other models.