TY - JOUR
T1 - Assessment of tissue injury in severe brain trauma
AU - Maggia, Christophe
AU - Doyle, Senan
AU - Forbes, Florence
AU - Heck, Olivier
AU - Troprès, Irène
AU - Berthet, Corentin
AU - Teyssier, Yann
AU - Velly, Lionel
AU - Payen, Jean François
AU - Dojat, Michel
N1 - Funding Information:
Grenoble MRI facility IRMaGe was partly funded by the French program Investissement d avenir run by the Agence Nationale pour la Recherche; grant Infrastructure d 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.
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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 postevent. To assess the diffuse aspect of the injury, we fuse several atlases to parcel cortical, subcortical andWM 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.
AB - 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 postevent. To assess the diffuse aspect of the injury, we fuse several atlases to parcel cortical, subcortical andWM 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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U2 - 10.1007/978-3-319-30858-6_6
DO - 10.1007/978-3-319-30858-6_6
M3 - Editorial
AN - SCOPUS:84961589364
SN - 0302-9743
VL - 9556
SP - 57
EP - 68
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 1st International Workshop on Brainlesion, Brainles 2015 Held in Conjunction with International Conference on Medical Image Computing for Computer-Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 5 October 2015
ER -