## 3 Results
### Comparative Experiment
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Dice Score** & **SDlogl** & \(J_{o}(u)\leq 0\) & **Sensitivity** & **Time (s)** \\ \hline
**Mindboggle101:** & & & & & \\ Affine & \(0.385\pm 0.021\) & – & – & – & – \\ SyN & \(0.508\pm 0.055\) & \(0.581\pm 0.075\) & \(1297\pm 982\) & 44.517 & 781.543 \\ LDDMM & \(0.492\pm 0.048\) & \(0.612\pm 0.081\) & \(1156\pm 652\) & 43.371 & 86.837 \\ DIF-VM & \(0.538\pm 0.105\) & \(0.531\pm 0.121\) & \(13,523\pm 2351\) & 43.267 & 0.517 \\ VoxelMorph-1 & \(0.581\pm 0.063\) & \(0.502\pm 0.037\) & \(51,415\pm 8463\) & 46.812 & 0.092 \\ VoxelMorph-2 & \(0.603\pm 0.020\) & \(0.498\pm 0.058\) & \(48,906\pm 7183\) & 45.785 & 0.264 \\ FAIM & \(0.619\pm 0.016\) & \(0.564\pm 0.052\) & \(11,975\pm 3085\) & 43.123 & 0.261 \\ Transmorph & \(0.635\pm 0.046\) & \(0.477\pm 0.041\) & \(11,703\pm 1652\) & 46.227 & 0.423 \\ MCANet & \(0.637\pm 0.013\) & \(0.445\pm 0.036\) & \(11,693\pm 2418\) & 47.551 & 0.733 \\ \hline
**Neurite OASIS:** & & & & & \\ Affine & \(0.532\pm 0.151\) & – & – & – & – \\ SyN & \(0.663\pm 0.071\) & \(0.575\pm 0.155\) & \(1786\pm 874\) & 49.486 & 1058 \\ LDDMM & \(0.584\pm 0.068\) & \(0.533\pm 0.07\) & \(1569\pm 528\) & 45.283 & 123.765 \\ DIE-VM & \(0.636\pm 0.121\) & \(0.541\pm 0.126\) & \(14,875\pm 1985\) & 44.916 & 0.863 \\ VoxelMorph-1 & \(0.718\pm 0.060\) & \(0.421\pm 0.054\) & \(52,503\pm 8762\) & 51.854 & 0.568 \\ VoxelMorph-2 & \(0.732\pm 0.031\) & \(0.417\pm 0.048\) & \(47,452\pm 6827\) & 50.349 & 0.576 \\ FAIM & \(0.736\pm 0.018\) & \(0.524\pm 0.032\) & \(11,825\pm 2843\) & 46.786 & 0.412 \\ Transmorph & \(0.764\pm 0.034\) & \(0.462\pm 0.036\) & \(11,681\pm 2631\) & 49.281 & 0.732 \\ MCANet & \(0.762\pm 0.023\) & \(0.405\pm 0.076\) & \(11,951\pm 1493\) & 54.641 & 0.917 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Registration results of Affine, SyN, LDDMM, DIF-VM,VoxelMorph-1, VoxelMorph-2, FAIM, Transmorph, and MCANet. while maintaining high sensitivity. Although deep-learning methods such as MCANet perform well in accuracy and smoothness, they usually require a significant amount of training data and computing resources and are sensitive to changes in data distribution. However, MCANet has found an optimal balance point in these challenges, which can efficiently provide high-precision and natural registration results. The boxplot in Figure 4 intuitively shows the advantages of MCANet in different indicators, especially in Dice score and SDlogJ, proving its effectiveness and reliability in image registration tasks. While traditional algorithms exhibit advantages in deformation field smoothness, this performance is contingent upon meticulous parameter tuning. Our experiments reveal that, as the gradient step in SyN increases, registration accuracy improves, but so does the deformation field folding rate, increasing linearly. This indicates that traditional algorithms may generate overly complex and uninterpretable deformation results with higher parameter settings. In contrast, MCANet not only surpasses traditional algorithms in registration speed but also in accuracy. Among the deep-learning methods, although MCANet often achieves higher or comparable Dice scores, it is not without drawbacks. Deep-learning algorithms typically require substantial training data and computational resources, and their performance can be sensitive to variations in data distribution. In comparison with Transmorph and DIF-VM, MCANet usually obtains higher or comparable Dice scores as well as lower SDlogJ, which indicates that MCANet usually has higher accuracy and smoother deformation fields. Deep-learning algorithms typically require large amounts of training data and computational resources, and their performance is very sensitive to changes in data distribution. Thus, the complexity of these models sometimes leads to longer training times and higher hardware requirements. Despite these challenges, MCANet achieves an optimal balance, efficiently delivering highly accurate and natural registration results.