Abstract
This paper presents a novel approach aiming at improving the White Matter (WM) fiber-bundle extraction approach described in (Stamile C et al Brain Informatics and Health: 8th International Conference, BIH, 2015). This provides anatomically coherent fiber-bundles, but it is unable to distinguish symmetric fiber-bundles. The new approach we are proposing here overcomes this limitation by integrating QuickBundles (QB) into it. As a matter of fact, QB has features complementary to those of the approach of (Stamile C et al Brain Informatics and Health: 8th International Conference, BIH, 2015), because it is capable of distinguishing symmetric fiber-bundles but, often, it does not return anatomically coherent fiber-bundles. We also present some experiments showing that the Precision, the Recall and the F-Measure of this new approach improve by 9.76, 3.08 and 8.96%, compared to the corresponding ones of the approach of (Stamile C et al Brain Informatics and Health: 8th International Conference, BIH, 2015), which, in their turn, were shown to be better than the ones of QB.
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Acknowledgements
This research was supported by EU MC ITN TRANSACT 2012 #316679. This work was partially supported by Aubay Italia S.p.A.
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Cauteruccio, F., Stamile, C., Terracina, G., Ursino, D., Sappey-Marinier, D. (2018). Integrating QuickBundles into a Model-Guided Approach for Extracting “Anatomically-Coherent” and “Symmetry-Aware” White Matter Fiber-Bundles. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_4
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DOI: https://doi.org/10.1007/978-3-319-56904-8_4
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