## 3 Results and Discussion
### Model Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & **Model** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **Average** \\ \hline \multirow{8}{*}{\(P\)} & UNet & 0.741 & 0.660 & 0.635 & 0.763 & 0.732 & 0.685 & 0.645 & 0.694 \\  & 3D-UNet & 0.710 & 0.535 & 0.687 & 0.734 & 0.617 & 0.617 & 0.544 & 0.636 \\  & UNetPlus & 0.674 & 0.690 & 0.561 & 0.681 & 0.509 & 0.667 & 0.599 & 0.639 \\  & UNet++ & 0.718 & 0.716 & 0.655 & 0.756 & 0.545 & 0.635 & 0.628 & 0.664 \\  & SWSDS-Net & 0.789 & 0.775 & 0.702 & 0.811 & 0.564 & 0.692 & 0.647 & 0.712 \\ \hline \multirow{8}{*}{\(R\)} & UNet & 0.387 & 0.667 & 0.653 & 0.391 & 0.495 & 0.577 & 0.403 & 0.510 \\  & 3D-UNet & 0.453 & 0.613 & 0.593 & 0.512 & 0.599 & 0.675 & 0.538 & 0.570 \\  & UNetPlus & 0.210 & 0.471 & 0.535 & 0.095 & 0.497 & 0.356 & 0.178 & 0.334 \\  & UNet++ & 0.521 & 0.662 & 0.702 & 0.552 & 0.703 & 0.727 & 0.678 & 0.653 \\  & SWSDS-Net & 0.534 & 0.642 & 0.701 & 0.512 & 0.658 & 0.714 & 0.503 & 0.608 \\ \hline \multirow{8}{*}{\(I\)} & UNet & 0.615 & 0.705 & 0.682 & 0.644 & 0.670 & 0.693 & 0.634 & 0.663 \\  & 3D-UNet & 0.636 & 0.650 & 0.687 & 0.681 & 0.684 & 0.703 & 0.652 & 0.670 \\  & UNetPlus & 0.530 & 0.647 & 0.633 & 0.503 & 0.626 & 0.618 & 0.537 & 0.584 \\  & UNet++ & 0.645 & 0.710 & 0.694 & 0.663 & 0.633 & 0.717 & 0.643 & 0.672 \\  & SWSDS-Net & 0.675 & 0.696 & 0.692 & 0.661 & 0.667 & 0.707 & 0.640 & 0.676 \\ \hline \multirow{8}{*}{\(D\)} & UNet & 0.509 & 0.664 & 0.644 & 0.517 & 0.591 & 0.626 & 0.496 & 0.578 \\  & 3D-UNet & 0.553 & 0.571 & 0.637 & 0.603 & 0.608 & 0.645 & 0.541 & 0.594 \\ \cline{1-1}  & UNetPlus & 0.320 & 0.599 & 0.548 & 0.167 & 0.503 & 0.464 & 0.274 & 0.411 \\ \cline{1-1}  & UNet++ & 0.604 & 0.688 & 0.678 & 0.638 & 0.614 & 0.678 & 0.652 & 0.650 \\ \cline{1-1}  & SWSDS-Net & 0.637 & 0.702 & 0.702 & 0.628 & 0.607 & 0.703 & 0.566 & 0.649 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results of each model. from the encoder undergo upsampling and are concatenated with the feature maps from the decoder. Subsequently, the SWS module is reintegrated into the encoder to further enhance its representational capacity. This design effectively exploits SWS's fully 3D attention mechanism, thereby improving segmentation accuracy. The figures depicting segmentation results demonstrate that SWSDS-Net yields minimal noise and interference in segmented targets. Through objective network performance comparisons, segmentation result analysis, and subjective visual evaluation, SWSDS-Net's effectiveness is convincingly demonstrated. In Figure 4, after analyzing the segmentation results, it is evident that all of these networks perform admirably when dealing with simpler scenes characterized by minimal noise and interference. However, in more intricate scenarios, UNetPlus exhibits a higher susceptibility to noise interference, resulting in notable false positives and misdetections. This vulnerability can be attributed to the complex structure of UNetPlus, which incorporates nested UNet structures and deep supervision mechanisms. Consequently, the model tends to overfit noise and struggles to effectively differentiate between fracture features and noise features, thereby compromising segmentation accuracy. Clearly outperforming the other models considered here, SWSDS-Net demonstrates superior segmentation performance. The SWS attention mechanism ensures equitable attention allocation and enhancement for both large and small objects, maximizing the detection of small targets without any omissions. Moreover, DSCN's deformable properties enable adaptive sampling based on fracture shapes, facilitating improved extraction of fracture features. Additionally, SWSDS-Net adeptly captures intricate details, leading to near-perfect segmentation of fracture shapes while avoiding issues such as blurred edges or inaccurate shapes.