TractoInferno: A large-scale, open-source, multi-site database for machine learning dMRI tractography

# TractoInferno Machine Learning Tractography Dataset The /derivatives folder contains the pre-split training/validation/testing datasets, each containing unique subjects with the following: - T1W image - DTI metrics maps (FA/AD/MD/RD) - DWI image with bval/bvec - fodf map + fodf peaks - White matter/grey matter/csf masks - DWI SH map (SH of order 6 fitted to the DWI signal, using the `descoteaux07` basis: https://dipy.org/documentation/1.3.0./theory/sh_basis/) - Tractograms of the following delineated bundles - AF_L - AF_R - CC_Fr_1 - CC_Fr_2 - CC_Oc - CC_Pa - CC_Pr_Po - CG_L - CG_R - FAT_L - FAT_R - FPT_L - FPT_R - IFOF_L - IFOF_R - ILF_L - ILF_R - MCP - MdLF_L - MdLF_R - OR_ML_L - OR_ML_R - POPT_L - POPT_R - PYT_L - PYT_R - SLF_L - SLF_R - UF_L - UF_R All tractograms contain compressed streamlines to reduce disk space, which means that the step size is variable. If your algorithm requires a fixed step size, you have to manually resample the streamlines, which can be done using SCILPY (https://github.com/scilus/scilpy) and the scil_resample_streamlines.py script: https://github.com/scilus/scilpy/blob/master/scripts/scil_resample_streamlines.py To evaluate a candidate tractogram, refer to: https://github.com/scil-vital/TractoInferno/