## 1 Introduction
### Motivation  
As mentioned previously, the vast majority of thalamus-segmentation methods use exclusively T1-weighted images [6; 15; 16; 17; 18; 19; 20; 29], an MR sequence that fails to present a good contrast on the thalamus borders (Figure 3-T1). In contrast, diffusion MRI naturally accounts for the different properties of the brain's microstructure [28; 30; 31; 32; 33], leading to higher contrast for certain sub-cortical structures, and consequently greater potential as a tool for segmentation problems.  
For example, when visualizing scalar maps computed from diffusion tensor imaging (Figure 3), the frontiers of the thalamus with other structures are clearly visible: on FA maps, for instance, the boundary between the thalamus and internal capsule is shown with a higher contrast due to the differing anisotropic diffusivity of these structures.  
Another indicator of the potential of dMRI for thalamus segmentation is the use of diffusion indices to parcel the thalamus [2; 34; 24]. In summary, the parcellation of thalamic nuclei is reliable only when using diffusion MRI.  
Considering that dMRI can highlight more differences between the thalamus and its neighboring structures than other MRI contrasts, it may even lead to a more appropriate  
Figure 3: Axial slices in the thalamus region with thalamic borders highlighted (areas pointed by the arrows): T1-weighted image and examples of diffusion indices (fractional anisotropy, mean diffusivity, and axial diffusivity) from diffusion tensor imaging. Adapted with permission from Ref. [26]. 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).  
delineation of the actual structure, thereby pushing the theoretical limits of segmentation performance for both manual and automatic methods.  
Despite being able to carry a higher contrast for certain brain structures, dMRI presents some disadvantages, including: an excessive acquisition time in the MR scanner; typically lower spatial resolution compared to T1-weighted images; a high computational complexity owing to multiple diffusion directions; complex registration in cases where T1-weighted images or T1-derived annotations are needed simultaneously; and extra non-trivial processing steps [31; 32], as in the cases of geometrical corrections and model fitting.  
One of the primary motivations for the use of CNNs is a significant decrease in prediction time compared to other available methods. For instance, the segmentation of a whole brain can be achieved in a matter of seconds--i.e., QuickNAT requires 20 s when using a GPU--whereas atlas-based methods usually demand hours. Furthermore, the latest CNN methods represent the current state-of-the-art in terms of overlap metrics for segmenting brain structures [17; 35].  
The main downside of these approaches is the requirement for sufficiently large annotated datasets. Furthermore, the performance of these methods is closely linked to the quality of the data labels [36]. In addition, the input data must be diverse and consistent to ensure satisfactory CNN performance. Specifically, the data and labels require appropriate standardization, especially when working with multimodal data [37].  
Despite the high performance of segmentation methods using T1-weighted images, prior studies have demonstrated the great potential of dMRI for improving the quality of thalamic segmentation [2; 24; 26; 34]. However, preparing the dMRI to be used in conjunction with CNNs for this purpose is not a trivial task. Accordingly, a primary motivation of this work is the need for an appropriate dMRI processing pipeline for CNN approaches, and there are the additional objectives of providing the preprocessing methodology, preprocessed data, and a benchmark test set to facilitate comparisons with future works.