## 4 Discussion  
Fine-tuning represents an effective approach when adjusting a model to manually segmented data. Prior to fine-tuning, our CNN models produced inferior results compared to those achieved by the atlas-based methods and QuickNAT segmentation. However, the fine-tuning process yielded an absolute improvement of approximately 0.12 in the Dice coefficient. It is important to remember that the encoder was frozen throughout the fine-tuning phase, and only the decoder weights that reconstructed the segmentation were trained. Although minor differences in metrics were observed when aggregating all input, the diffusion indices computed from DTI clearly improved the quality of thalamus segmentation when used as input channels to the CNN. These differences in metrics were more significant for the FA map and combination of all available maps when compared to the case of using T1-weighted images alone as input. Further improvements may be obtained by extending the proposed approach to other microstructural indices [65], especially when multi-shell data, such as NODDI and MAP-MRI, are available, as for HCP. As a single-input channel, T1-weighted achieved a Dice coefficient of approximately 0.88, representing the current state-of-the-art in thalamus segmentation, as reported in the original QuickNAT work [17]. Therefore, the proposed method surpasses state-of-the-art performance by incorporating dMRI, showing that aggregated multimodal information is beneficial for the thalamus-segmentation task. The DC attained by our reproduction of the QuickNAT was in the range of 0.79, as opposed to the result of 0.88 reported by [17]. However, we stress that the QuickNAT model was not fine-tuned to this specific dataset, and the only addition to the framework was the \"FreeSurfer conform\", as it is a requirement for the QuickNAT pipeline. Among the three possible views, the sagittal view yielded the lowest dice coefficient. A possible reason for this occurrence is the lack of thalamus symmetry under this view, which restricts the horizontal-reflection technique. Furthermore, the thalamus shape is more elongated in the anteroposterior axis, favoring the partial volume issue being more pronounced in this view. This also explains the best segmentation quality in the coronal view, where the thalamus exhibits the smallest cross section. Figure 11: Thalamus segmentation: ground truth (manual segmentation) in red, silver standard (STAPLE) in blue on the (**left**), and CNN prediction (with the best model—all input channels combined) in blue on the (**right**). Our best results were obtained when using all input maps simultaneously (diffusion combined with T1). However, certain specific combinations unexpectedly led to deteriorated performance; i.e., the combination of the FA and MD yielded worse results than either of its constituent maps. We hypothesize that the aggregation of maps with small statistical differences is disadvantageous for the CNN model, as the resultant increase in information does not outweigh the increase in complexity. However, further investigation is required to understand this mechanism and determine the optimal map combinations for this task. Although the FSL masks yield better validation metrics compared to other automatic masks, they were not selected as the silver standard during the training phase. Instead, the masks created by combining FSL, FreeSurfer, and QuickNAT via STAPLE were used for this purpose, as the combination of atlas- and CNN-based methods is more likely to eliminate model bias. As all CNN models used and shared throughout this study were trained and evaluated on these specific benchmark data, we do not expect identical results if they are applied directly to other datasets. This is one of the limitations of the presented method. To successfully use our models and framework with other data, we recommended retraining or fine-tuning the models in the new data domain. With appropriate fine-tuning, the model is expected to yield comparable performance irrespective of dataset.