Abstract
We report our methodological developments to investigate, in a multi-center study using mean diffusivity, the tissue damage caused by a severe traumatic brain injury (GSC \(<9\)) in the 10 days post-event. To assess the diffuse aspect of the injury, we fuse several atlases to parcel cortical, subcortical and WM structures into well identified regions where MD values are computed and compared to normative values. We used P-LOCUS to provide brain tissue segmentation and exclude voxels labeled as CSF, ventricles and hemorrhagic lesion and then automatically detect the lesion load. Preliminary results demonstrate that our method is coherent with expert opinion in the identification of lesions. We outline the challenges posed in automatic analysis for TBI.
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Between 2004–2014, more than 500 papers were published on lesion segmentation for each of these pathologies and only 53 for TBI. Source WebOfScience with keywords: Brain and MRI and (Segmentation or Classification) and ‘Pathology’.
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Acknowledgments
Grenoble MRI facility IRMaGe was partly funded by the French program Investissement d\(^{\prime }\) avenir run by the Agence Nationale pour la Recherche; grant Infrastructure d\(^{\prime }\) avenir en Biologie Santé - ANR-11-INBS-0006. Research funded by French ministry of research and education under the Projet Hospitalier de Recherche Clinique grant OXY-TC to JFP.
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Maggia, C. et al. (2016). Assessment of Tissue Injury in Severe Brain Trauma. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_6
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DOI: https://doi.org/10.1007/978-3-319-30858-6_6
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