Abstract #2843
Megatrack: A fast and effective strategy for group comparison and supervised analysis of large-scale tractography datasets
Flavio Dell'Acqua 1 , Luis Lacerda 1 , Rachel Barrett 1 , Lucio D'Anna 2 , Stella Tsermentseli 3 , Laura Goldstein 4 , and Marco Catani 2
1
Dept of Neuroimaging, King's College London,
London, United Kingdom,
2
Dept
of Forensic and Neurodevelopmental Sciences, King's
College London, London, United Kingdom,
3
Dept
of Psychology, University of Greenwich, London, United
Kingdom,
4
Dept
of Psychology, King's College London, United Kingdom
While manual dissections of tractography datasets may
offer the best results in terms of anatomical accuracy,
they are also extremely time consuming making difficult
to extend them to large-scale datasets. On the contrary,
automatic dissections or clustering approaches allow
researcher to efficiently dissect large numbers of
datasets but at the expense of decreased accuracy in the
final dissection, leaving often little to no user
interaction to control for artifactual components. In
this study we propose a novel approach that drastically
reduces the time required for manual dissections while
preserving the ability to extract automatically tract
specific measures from large datasets
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