DOI: 10.5281/zenodo.7729059
title: Population average atlas for RecobundlesX

description: Multi-atlas bundle segmentation

This data is made to be used with the following script:https://github.com/scilus/scilpy/blob/master/scripts/scil_recognize_multi_bundles.pyOr the following nextflow pipeline:https://github.com/scilus/rbx_flow

This script is in fact a multi-atlas, multi-parameters version of Garyfallidis et al. (2018) with labels fusions. We name this algorithm RecobundlesX. This version facilitate parameters exploration and stabilize/robustify the results from Recobundles.



Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-based registration and clustering." NeuroImage  170 (2018): 283-295.Rheault, François. "Analyse et reconstruction de faisceaux de la matière blanche." Computer Science (Université de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255


UsageHere is an example (for more details use `scil_recognize_multi_bundles.py -h`) :

antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4scil_recognize_multi_bundles.py ${TRACTOGRAM} default_config.json atlas/*/ output0GenericAffine.mat --out_dir ${OUTPUT_DIR}/ --log_level DEBUG --multi_parameters 27 --minimal_vote 0.4 --tractogram_clustering 8 10 12 --processes 8 --seeds 0 --inverse -f

To facilitate interpretation, all endpoints were uniformized head/tail. To see, which side of a bundle is head or tail, you can load the atlas bundle into the software MI-Brain

Notes on bundles- AC and PC were added mostly in case the atlas is used for lesion-mapping or figures. Likely, segmentation won't produce good results. This is mostly due to difficult tracking for these bundles.- The CC are split for each lobe. However, for technical consideration, the frontal portion was split in two to facilitate clustering and segmentation. For the same reason, the portion fanning to the pre/post central gyri were separated.- The streamlines present in the CC are homotopic, Recobundles will allow for variation and thus lead to 'some' heterotopy. However, it is expected that the results will be mostly homotopic.- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track and is often missing.- FPT and POPT should terminate in the pons. However, to fully capture candidate streamlines and improve segmentation quality even streamlines reaching down the brainstem are selected. - PYT should reach down the brainstem. For similar reasons to the FPT/POPT, streamlines ending in the pons are selected. Otherwise, fanning is affected and bundles is too skinny. - OR_ML will most likely have difficulty capturing the full ML. However, this is often due to difficult tracking.- The cerebellum is often cut due to acquisition FOV. In such a case, all projection bundles will be more difficult to recognize and most cerebellum bundles will be missing (ICP, MCP, SCP).

See Mosaic of bundles here.

AcronymAC - Anterior commisureAF - Arcuate fasciculusCC_Fr_1 - Corpus callosum, Frontal lobe (most anterior part)CC_Fr_2 - Corpus callosum, Frontal lobe (most posterior part)CC_Oc - Corpus callosum, Occipital lobeCC_Pa - Corpus callosum, Parietal lobeCC_Pr_Po - Corpus callosum, Pre/Post central gyriCC_Te - Corpus callosum, Temporal lobeCG - CingulumFAT - Frontal aslant tractFPT - Fronto-pontine tractFX - FornixICP - Inferior cerebellar peduncleIFOF - Inferior fronto-occipital fasciculusILF - Inferior longitudinal fasciculusMCP - Middle cerebellar peduncleMdLF - Middle longitudinal fascicleOR_ML - Optic radiation and Meyer's loopPC - Posterior commisurePOPT - parieto-occipito pontine tractPYT - Pyramidal tractSCP - Superior cerebellar peduncleSLF - Superior longitudinal fasciculusUF - Uncinate fasciculus