Introduction
death, EAH, IVH, perilesional oedema, injuries, traumatic, TBI, TBI lesions, hematoma, extra-axial hematoma, disability, Traumatic Brain Injury
HEMATOMA, INTRAVENTRICULAR HEMORRHAGE
Traumatic Brain Injury (TBI) is a leading cause of death and disability in the world. Despite significant progress in their management, half of severe TBI patients will have long-term disabilities. One big issue is to have reliable tools to predict patient outcomes after TBIThe BLAST-CT algorithm, developed to automatically delineate intraparenchymal hematoma (IPH), extra-axial hematoma (EAH), intraventricular hemorrhage (IVH) and perilesional oedema (Od) after severe TBI, is to our knowledge the most advanced segmentation tool of TBI lesions on CT-scansWhile predicting TBI patient outcome at 6 months using qualitative analysis of CT scan may be difficult, information contained in initial CT-scan could be used to predict short-term evolution such as the intensity of therapies required for each patient. In severe TBI patients, most therapies are directed to control intracranial pressure (ICP), a strong driver of outcome after severe TBI. Eight ICP-treatment modalities have been then collected to validate a daily scoring system, the TIL sum with a maximum score of 38 pointsWe hypothesized that an automated delineation of the most frequent traumatic brain lesions from initial CT-scan could predict a moderate-to-severe TIL sum assessed during the first week after admission to the intensive care unit (ICU).In this study, we extracted metrics from brain CT-scans representing the volume, the type and the spatial location of injuries using automatic or manual segmentations. Two transfer learning strategies were used to re-train the BLAST-CT algorithm in order to improve the automatic segmentations. Finally, segmentation and classification models were validated using internal and external datasets of patients.