## 1 Introduction
Some exceptional methods [6; 19; 20] can obtain good segmentation results even with smaller datasets by using artifices such as multi-scale patches. However, generalizability cannot be ensured given an insufficient test set. One of the most recent and high-performing deep learning techniques is QuickNAT [17], which employs a Bayesian fully-convolutional neural network to estimate the thalamus shape from T1-weighted MR images, requiring only 20 s when using a GPU. This method uses multiple large datasets to ensure consistency across different MRI data. The advantages derived from multimodality warrant further investigation. Indeed, the multimodality of MRI has enabled highly accurate performance, reaching Dice coefficients of 0.878 and 0.890 for the two thalami when calculated against manual segmentation [21]. Specifically, the random forest algorithm was employed to generate predictions based on structural (T1 and T2-weighted) and diffusion-weighted MRI. In particular, diffusion MRI (dMRI) alone has been frequently employed in prior studies to perform this task [21; 22; 23]. Among the most recently proposed works, [PERSON] et al. [24] took advantage of the orientation-distribution functions calculated using the spherical harmonic model to detect and characterize the thalamus with satisfactory performance. In fact, the inherent cytological differences within the thalamus serve as the basis for the hypothesis of dMRI's sensitivity to the microstructural modulations of this structure. By relying on the Fourier transform that relates the dMRI signal to the propagator from which the orientation-distribution function is calculated, information regarding the brain tissue architecture can be inferred for each voxel [25]. This is possible because the propagator represents the probability that the water volume inside the brain undergoes displacement \(r\) in diffusion time \(\tau\), which reflects the diffusion process constrained by the walls of the compartment therein (e.g., the white matter axon). The use of dMRI to obtain microstructural details of brain tissue offers the potential to enable more robust and accurate segmentation procedures. Despite the significant potential of deep learning methods and the use of diffusion data for thalamus segmentation, few attempts have been made to combine the two approaches for that purpose [26; 27]. One reason behind the scarcity of research pertaining to this subject is the complexity of using multimodality on CNNs, specifically with dMRI. Additional steps demanded by the use of dMRI data with CNNs include: fitting a diffusion model and computation of diffusion tensor maps; complex registration processes among different MRI sequences; changes in the CNN architecture to handle multiple inputs; manipulation of diffusion tensor maps. All of these steps require a rigorous methodology to express the advantages of diffusion data. Additional challenges, such as the scarcity of large datasets and ground truth (manually annotated segmentations), are more pronounced in dMRI, as the acquisition is more costly and the interpretation of diffusion data is not as trivial as it is on T1-weighted images [28]. The primary objectives of this study were to provide a benchmark for the development of more precise thalamus-segmentation methods, taking advantage of diffusion MRI data; promote fair comparisons among different methods; and interpret the contribution of dMRI to the segmentation task. The main contributions of this work are: a large processed benchmark dataset composed of annotated co-registered T1-weighted and diffusion MRI; a set of different thalamic masks for each subject computed using atlas- and CNN-based methods and statistically-combined masks; manual ground-truth data provided by experts; a baseline framework useful in the processing of diffusion data and additional training of CNNs for thalamus segmentation; and an ablation study on the contribution of dMRI to this task.