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3 results for “RecobundlesX”
Population average atlas for RecobundlesX (BundleSeg) - TractSeg Definitions
<p><strong>Multi-atlas bundle segmentation</strong></p> <p>This data is made to be used with the following script:<br><a href="https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py">https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py</a></p> <blockquote> <p><br>Etienne St-Onge, Kurt Schilling, Francois Rheault, "BundleSeg: A versatile, reliable and reproducible approach to whitte matter bundle segmentation.", arXiv, 2308.10958 (2023)<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</p> </blockquote> <p><strong>Usage</strong><br>Here is an example (for more details use `scil_tractogram_segment_with_bundleseg.py -h`) :</p> <p><code>antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4</code><br><code>scil_tractogram_segment_with_bundleseg.py ${TRACTOGRAM} config_fss_1.json atlas/ output0GenericAffine.mat --out_dir ${OUTPUT_DIR}/ --log_level DEBUG --processes 8 --seeds 0 --inverse -f</code><br><br></p> <p>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 <a href="https://github.com/imeka/mi-brain">https://github.com/imeka/mi-brain</a></p> <p>(If you are processing multiple subjects, this pipeline could be useful for you <a href="https://github.com/scilus/rbx_flow">https://github.com/scilus/rbx_flow</a>)</p> <p><strong>Notes on bundles</strong><br>- The bundles follow the overall anatomical definition of TractSeg (initially from TractQuerier) but are a heavily processed union to discard false positives, outliers, unrealistic paths, etc.<br>- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track <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).<br>- All the bundles starting with T_ (Thalamo) or ST_ (Striato) are based on region of interest, and are not usually part of classical major pathways.</p>
Atlas of false-positive bundles for RecobundlesX
<p>This data is made to be used with the <a href="https://zenodo.org/record/4630660">Atlas for RecobundlesX</a>.</p> <p>In the study performed by <em>MaierHein et al. (2017)</em> about the ISMRM 2015 Tractography Challenge, several bundles from the submitted tractograms were labeled as false positives because they were not in the ground-truth tractogram. Quality control was performed on these bundles to select those with the most anatomical implausible trajectory. From these bundles, 44 were selected and refined to obtain smooth bundle models.</p> <p>v2. Streamlines outliers that were not removed with an automatic pruning step were removed manually.</p> <p><em>Maier-Hein KH, Neher PF, Houde J-C, Côté M-A, Garyfallidis E, Zhong J, et al. 2017. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 8:1349.</em></p>
Atlas for RecobundlesX
<p><strong>Multi-atlas bundle segmentation</strong><br>(This is an older version, consider using <strong><a href="https://zenodo.org/records/7729059">this</a></strong> instead)</p> <p>This data is made to be used with the following script: <a href="https://github.com/scilus/scilpy/blob/1.5.0/scripts/scil_recognize_multi_bundles.py">scil_recognize_multi_bundles.py</a><br>This <a href="https://github.com/scilus/rbxflow">Nextflow pipeline</a> is made to simplify the execution.</p> <p>This script is in fact a multi-atlas, multi-parameters version of <em>Garyfallidis et al. (2018)</em> with labels fusions. We name this algorithm RecobundlesX. If you have any questions, email <em>francois.rheault@gmail.com.</em></p> <p><em>Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-based registration and clustering." NeuroImage 170 (2018): 283-295.</em><br><br>Also for more details, a thesis chapter is available in French (chapter 4) with more details.<br><em>Rheault, Francois, "Analyse et reconstruction de faisceaux de la matière blanche", (2020), Computer Science (Université de Sherbrooke), pp. 258. https://savoirs.usherbrooke.ca/handle/11143/17255</em></p>
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