## 2 Materials and Methods
### Data Processing  
The HCP dataset was processed to ensure its compatibility with CNN training. Specifically, minimal steps were taken to standardize and organize the data for use. The processed benchmark dataset was subsequently released for public access.  
Indices describing the brain microstructure were derived from the dMRI data by reconstructing signals using a diffusion tensor imaging (DTI) model [43]. These models are frequently employed in clinical activity under the assumption that each diffusion signal coming from the brain tissue can be described by a Gaussian function. The dMRI signal fitting is therefore obtained by calculating the tensor, a symmetric 3 \(\times\) 3 matrix whose parameters can be derived from the MR volumes acquired in different gradient directions. Such a tensor is a representation of the 3D diffusion process for any voxel of the 3D volume, and can be expressed via eigenvalue and eigenvector formulation.  
The microstructural indices [44; 45] used throughout this study were computed from such eigenvalues. These indices were: axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), and fractional anisotropy (FA). All relevant computations were performed using the DIPY library [46] with the weighted least squares method. Only those dMRI volume images corresponding to a b-value of 1000 s/mm\({}^{2}\) (single shell DTI model) were selected. This process generated four 3D volumes, one for each microstructural index, for all subjects of the cohort.  
Another crucial process applied to the data is the registration, which placed the diffusion- and T1-weighted images, and the masks, within a single space, allowing them to be simultaneously used by the CNN.  
Although the HCP dataset comes with a T1w image registered to a diffusion space, it does not provide an appropriate affine matrix. Therefore, we computed the registration matrix (rigid transformation) using the T1w images on the T1 and diffusion spaces. Subsequently, the matrix was used to translate the masks to the diffusion space. It is important to emphasize that the registration was visually inspected in 20% of the dataset, including all subjects with manual segmentation. Likewise, all registration logs were inspected; the two discarded subjects were flagged.  
The decision to transport the T1-weighted images to the diffusion space, rather than the other way around, was made because the scalar T1-weighted images do not suffer as much from voxel interpolation during the registration process as tensor images would. Since the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Comparison** & **DC (SD)** & **FNR** & **FPR** & **VS** & **AHD (SD)** \\ \hline expert vs. non-expert & 0.8022 (0.0804) & 0.1228 & 0.2472 & 0.1555 & 0.2501 (0.0573) \\ \hline expert vs. STAPLE & 0.7880 (0.0310) & 0.0070 & 0.3459 & 0.4128 & 0.2190 (0.0340) \\ \hline non-expert vs. STAPLE & 0.7108 (0.0655) & 0.0129 & 0.4397 & 0.5577 & 0.3685 (0.0971) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inter rater comparison: DC—Dice coefficient; FNR—false negative ratio; VS—volumetric similarity; AHD—average Hausdorff distance (in voxel size).  
masks are integer values representing structures of the brain, they were interpolated with the nearest-neighbor technique.