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Abstract #0852

TractLearn: a geodesic learning framework for quantitative dissection of brain bundles

Arnaud Attyé1,2, Felix Renard3, Monica Baciu2, Elise Roger2, Laurent Lamalle4, Patrick Dehail5, Hélène Cassoudesalle5, and Fernando Calamante6,7
1School of Biomedical Engineering, University of Sydney, Sydney, Australia, 2CNRS LPNC UMR 5105, University of Grenoble Alpes, Grenoble, France, 3Laboratoire d'informatique de Grenoble, Grenoble, France, 4University of Grenoble Alpes, Grenoble, France, 5Bordeaux Universitary Hospital, Bordeaux, France, 6Sydney Imaging Lab, University of Sydney, Sydney, Australia, 7School of Aerospace, Mechanical and Mechatronic Engineering, Sydney, Australia

Here we present a unified framework for brain fascicles quantitative analyses by geodesic learning (TractLearn) — as a data-driven unsupervised learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles. Besides providing a framework to test the reliability of various brain metrics with a global overview, it allows to identify subtle quantitative alteration in disease model with small subset of patients and/or data sparsity. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.

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