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Section: Partnerships and Cooperations
Regional Initiatives
INCR: MUltiple Sclerosis Imaging Check-out (MUSIC)
Participants : Gilles Edan, Francesca Galassi, Olivier Commowick, Christian Barillot, Anne Kerbrat, Jean-Christophe Ferre.
The objective of this project is to investigate algorithms aimed at detecting, segmenting and following overtime the MS lesions, robustly enough to work on a multi-site clinical database. Methods are being evaluated on an amount of training and testing MS images with high quality segmentations from radiographers. The goal is to integrate the developed framework into a production workflow that will be employed by the clinical health network MUltiple Sclerosis Imaging Check-out (MUSIC), covering the western part of France.
ARED VARANASI
Participants : Christian Barillot, Camille Maumet, Xavier Rolland.
Thanks to the development of open science practices, more and more public datasets are available to the research community. In the field of brain imaging, these data, combined, bring a critical increase in sample size, necessary to build robust models of the typical and atypical brain. But, in order to build valid inferences on these data, we need to take into account their heterogeneity. Variability can arise due to multiple factors such as: differences in imaging instruments, in acquisitions protocols and even, in post-processing pipelines. In particular, the expansion of open source machine learning workflows creates a multitude of possible outputs out of the same dataset. The variations induced by this methodological plurality can be referred to as ‘analytic variability’ which will be the focus of the thesis funded in half by this ARED. The thesis will address two challenges: 1) How to combine neuroimaging data generated by different analysis pipelines? 2) How to publish neuroimages with an adequate level of metadata to enable their reuse? Methodological developments will combine machine learning techniques with methods from knowledge representation.