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BundleSeg: A Versatile, Reliable and Reproducible Approach to White Matter Bundle Segmentation

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Computational Diffusion MRI (CDMRI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14328))

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Abstract

This work presents BundleSeg, a reliable, reproducible, and fast method for extracting white matter pathways. The proposed method combines an iterative registration procedure to a recently developed precise streamline search algorithm that enables efficient segmentation of streamlines without the need for tractogram clustering or simplifying assumptions. We show that BundleSeg achieves improved repeatability and reproducibility than state-of-the-art segmentation methods, with significant speed improvements. The enhanced precision and reduced variability in extracting white matter connections offer a valuable tool for neuroinformatic studies, increasing the sensitivity and specificity of tractography-based studies of white matter pathways.

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Correspondence to Etienne St-Onge .

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St-Onge, E., Schilling, K.G., Rheault, F. (2023). BundleSeg: A Versatile, Reliable and Reproducible Approach to White Matter Bundle Segmentation. In: Karaman, M., Mito, R., Powell, E., Rheault, F., Winzeck, S. (eds) Computational Diffusion MRI. CDMRI 2023. Lecture Notes in Computer Science, vol 14328. Springer, Cham. https://doi.org/10.1007/978-3-031-47292-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-47292-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47291-6

  • Online ISBN: 978-3-031-47292-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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