## 3 Experiments and Results
### Input Maps Ablation Study  
As our primary hypothesis assumed that diffusion tensor maps can improve the quality of thalamus segmentation, the performances of models trained with each input map individually were compared to those of models trained with a combination of input maps. All models were trained using the data distribution described in Section 3.1. The reported results are the average metrics among the 20 subjects from the test set. The results (Table 4) indicate that the model trained on T1-weighted images yielded the worst results for every computed metric. While they are still within state-of-the-art levels, the worst Dice coefficients were obtained when the T1-weighted image was the only input channel (DC of 0.8837). The same consideration is valid for the average Hausdorff distance (AHD of 0.1230). In contrast, models trained using diffusion tensor maps as input (single or combined maps) exhibited improvements in Dice score. In fact, the worst result presented by a model trained with a single diffusion map was a Dice coefficient of 0.8925 (AD), and the only combined-channel model that yielded a Dice coefficient below 0.89 was that trained on MD and T1. Furthermore, all combined models produced superior results to those of the model using solely T1 images. Noteworthy results were also obtained by the model trained on the combination of all available maps. This combination, which can be regarded as the addition of T1 to the combination of all diffusion maps, improved the DC from 0.8904 to 0.8970. Nonetheless, if we consider the addition of diffusion indices to T1, the DC increases from 0.8837 to 0.8970, representing a much more substantial improvement. Regardless of interpretation, this  
Figure 8: Dice and average Hausdorff coefficients for each automatic segmentation method evaluated on the test set. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Not Fine-Tuned**} & \multicolumn{4}{c}{**Fine-Tuned**} \\ \cline{2-9}
**Input Maps** & **DC** & **sd DC** & **AHD** & **sd AHD** & **DC** & **sd DC** & **AHD** & **sd AHD** \\ \hline FA, MD, RD, AD, T1 & 0.7775 & 0.0223 & 0.2320 & 0.0253 & 0.8970 & 0.0116 & 0.1102 & 0.0129 \\ \hline FA, MD, RD, AD & 0.7721 & 0.0229 & 0.2389 & 0.0259 & 0.8904 & 0.0100 & 0.1158 & 0.0105 \\ \hline T1 & 0.7676 & 0.0242 & 0.2440 & 0.0272 & 0.8904 & 0.0154 & 0.1158 & 0.0157 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dice coefficient (DC), average Hausdorff distance (AHD) in voxel size, and their respective standard deviations (sd) for the test dataset before and after fine-tuning. combination yielded the best performance in terms of the evaluation metrics, indicating that the segmentation that uses all input channels exhibited more agreement with the ground truth. This result is intriguing, as T1 images were associated with decreased performance when combined with singular diffusion maps (FA and MD). Furthermore, results regarding the standard deviation reflect the segmentation stability for each map and certain combinations (Figure 9). Again, we note that models trained on T1-weighted images exhibited the highest dispersion in both DC and AHD. Nevertheless, among the models trained with a single input, that trained with FA, produced the best average and dispersion results. A statistical analysis was conducted through a two-sided Wilcoxon signed-rank test to compare the CNN models (Figure 9) and other methods (Figure 10) in terms of performance against our best model, namely, the model that used all diffusion maps in conjunction with T1-w images as input. We note that in addition to exhibiting better metrics when compared to other methods, our approach's results are significantly different, statistically. Identical analyses were performed to compare every CNN model with a single input channel (Table 5) to interpret their equivalences. From these tests, it is apparent that the statistical differences between the T1 models and any of the diffusion models are much more significant than