## 4 Experiment and Analysis
### Experimental Results  
To demonstrate the efficacy of CADyUNet on hippocampus segmentation tasks, some commonly used medical image segmentation models are selected to conduct comparison experiments on the HarP and MICCAI datasets, including 3D U-Net, Attention U-Net, UNETR, Swin UNETR, and our proposed model. The dice, the mIoU, and the F1 were used to analyze the experimental results. Tables 1 and 2 display the results of the contrastive experiment on the HarP and MICCAI datasets, respectively. As demonstrated by Tables 1 and 2, CADyUNet segments the hippocampus more accurately in the hippocampus segmentation task compared to other models. Compared to the baseline, the dice on the HarP dataset rose by 3.52%, the mIoU rose by 2.65%, and the F1 rose by 3.38%. On the MICCAI dataset, the dice, mIoU, and F1 score rose by 1.13%, 0.85%, and 1.08%, respectively. The results illustrate the efficacy of CADyUNet in hippocampus segmentation tasks. To show the hippocampus segmentation results of these algorithms more conveniently, the sagittal section, coronal section, and axial section segmentation results are provided in Figures 9, 10, and 11, respectively. The first column shows sections of the three dimensions of the input MRI image (axial, sagittal, and coronal), along with the corresponding hippocampus segmentation labels. The 3D U-Net segmentation results are displayed in the second column. The segmentation results obtained by the Attention U-Net, UNETR, Swin UNETR, and CADyUNet models are shown in the third, fourth, fifth, and last columns, respectively. As illustrated in these figures, in contrast to the outputs of other algorithms, the outputs of the CADyUNet model are closer to the standard segmentation labels (particularly for marginal hippocampus segmentation), which proves the efficacy of CADyUNet in hippocampus segmentation tasks. To show the model's efficacy more comprehensively, the Params, GFLOPs (giga floating-point operations), and FPS (frames per second) are also used to evaluate the model's performance. The experimental results are presented in Table 3. As shown in Table 3, the proposed CADyUNet significantly reduces the model's memory usage and computation usage while greatly increasing its inference speed compared with other models. The results presented in Tables 1-3 show that CADyUNet has better segmentation accuracy and uses the fewest computing resources on hippocampus segmentation tasks, which proves the superiority of our model. To identify the efficacy of the designed dy-block, the improved CA, and the introduction of the soft pool method in hippocampus segmentation tasks, five models are chosen  
Figure 11: The axial section of the label on the HarP dataset and the axial section of the output from 3D U-Net, Attention U-Net, UNETR, Swin UNETR, and CADyUNet models. Figure 10: The conoral section of the label on the HarP dataset and the conoral section of the output from 3D U-Net, Attention U-Net, UNETR, Swin UNETR, and CADyUNet models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params (M)** & **GFLOPs (G)** & **FPS (img/s)** \\ \hline
3D U-Net & 20.96 & 507.85 & 0.08 \\ Attention U-Net & 103.89 & 516.94 & 0.07 \\ UNETR & 92.29 & 32.25 & 0.84 \\ Swin UNETR & 15.51 & 37.48 & 0.18 \\ Ours & 1.05 & 24.18 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison results on the HarP dataset. to conduct ablation experiments on the HarP dataset and the MICCAI dataset: 3D U-Net, 3D U-Net + CA, 3D U-Net + improved CA, 3D U-Net + dy-lock, 3D U-Net + softpool, and CADyUNet. To preserve computing resources, the number of channels is reduced by four times based on the number of channels in the 3D U-Net model. Furthermore, the results of the ablation experiment are presented in Tables 4 and 5. Several useful conclusions can be drawn from the results. 1. All of the indicators increase as a result of the improved CA mechanism being used in 3D U-Net's skip connection.