DOI: 10.5281/zenodo.5165374
title: Population average atlas for RecobundlesX

description: <strong>Multi-atlas bundle segmentation</strong> This data is made to be used with the following script:<br> https://github.com/scilus/scilpy/blob/master/scripts/scil_recognize_multi_bundles.py<br> <br> Or the following nextflow pipeline:<br> 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.<br> <br> Rheault, François. "Analyse et reconstruction de faisceaux de la matière blanche." Computer Science (Université de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255 <strong>Usage</strong><br> Here 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 4`<br> `scil_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` <strong>Notes on bundles</strong><br> - 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.<br> - 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.<br> - 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.<br> - CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track and is often missing.<br> - 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. <br> - 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. <br> - OR_ML will most likely have difficulty capturing the full ML. However, this is often due to difficult tracking.<br> - 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 <strong>here</strong>. <strong>Acronym</strong><br> AC - Anterior commisure<br> AF - Arcuate fasciculus<br> CC_Fr_1 - Corpus callosum, Frontal lobe (most anterior part)<br> CC_Fr_2 - Corpus callosum, Frontal lobe (most posterior part)<br> CC_Oc - Corpus callosum, Occipital lobe<br> CC_Pa - Corpus callosum, Parietal lobe<br> CC_Pr_Po - Corpus callosum, Pre/Post central gyri<br> CC_Te - Corpus callosum, Temporal lobe<br> CG - Cingulum<br> FAT - Frontal aslant tract<br> FPT - Fronto-pontine tract<br> FX - Fornix<br> ICP - Inferior cerebellar peduncle<br> IFOF - Inferior fronto-occipital fasciculus<br> ILF - Inferior longitudinal fasciculus<br> MCP - Middle cerebellar peduncle<br> MdLF - Middle longitudinal fascicle<br> OR_ML - Optic radiation and Meyer's loop<br> PC - Posterior commisure<br> POPT - parieto-occipito pontine tract<br> PYT - Pyramidal tract<br> SCP - Superior cerebellar peduncle<br> SLF - Superior longitudinal fasciculus<br> UF - Uncinate fasciculus