Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes

Christophe Maggia (1) , Thomas Mistral (2) , Senan Doyle (3) , Florence Forbes (4) , Alexandre Krainik (1) , Damien Galanaud (5) , Emmanuelle Schmitt (6) , Stéphane Kremer (7) , Irène Troprès (8) , Emmanuel L. Barbier (1) , Jean-François Payen (1) , Michel Dojat (1)

1 GIN - [GIN] Grenoble Institut des Neurosciences
2 UGA UFRM - Université Grenoble Alpes - UFR Médecine
3 Pixyl Medical [Grenoble]
4 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
5 CHU Pitié-Salpêtrière [AP-HP]
6 CHRU Nancy - Centre Hospitalier Régional Universitaire de Nancy
7 Service de radiologie [Strasbourg]
8 IRMaGe - IRMaGe

We report the evaluation of an automated method for quantification of brain tissue damage, caused by a severe traumatic brain injury, using mean diffusivity computed from MR diffusion images. Our automatic results obtained on realistic phantoms and real patient images 10 days post-event provided by nine different centers were coherent with four expert manually identified lesions. For realistic phantoms automated method scores were equal to 0.77, 0.77 and 0.83 for Dice, Precision and Sensibility respectively compared to 0.78, 0.72 and 0.86 for the experts. The inter correlation class (ICC) was 0.79. For 7/9 real cases 0.57, 0.50 and 0.70 were respectively obtained for automated method compared to 0.60, 0.52 and 0.78 for experts with ICC=0.71. Additionally, we detail the quality control module used to pool data from various image provider centers. This study clearly demonstrates the validity of the proposed automated method to eventually compute in a multi-centre project, the lesional load following brain trauma based on MD changes.

Domaines: Statistiques [math.ST]

Christophe Maggia, Thomas Mistral, Senan Doyle, Florence Forbes, Alexandre Krainik, et al.. Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes (MICCAI), 10670, Springer, pp.88-99, 2018, Lecture Notes in Computer Science, ⟨10.1007/978-3-319-75238-9_8⟩. ⟨hal-01704679⟩