2022/08/08 by Margherita Rosnati, Rosnati, Margherita, Eyal Soreq +23
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Lesion #Medicine #Neuroimaging #Physical medicine and rehabilitation #Psychiatry #Radiological weapon #Radiology #Surgery #Trauma and Emergency Care Studies #Traumatic Brain Injury Research #Traumatic Brain Injury and Neurovascular Disturbances #Traumatic brain injury #cs.CV
paper · pdf · doi:10.48550/arxiv.2208.04114
published in arXiv (Cornell University) (Cornell University) · Accepted at MLCN MICCAI 2022 workshop
arxiv created 2022/08/08 · openalex publication_date 2022/08/08 · arxiv updated 2022/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics such as age, motor and pupil responsiveness, hypoxia and hypotension, and radiological findings on computed tomography (CT), have been identified as important variables for TBI outcome prediction. CT is the acute imaging modality of choice in clinical practice because of its acquisition speed and widespread availability. However, this modality is mainly used for qualitative and semi-quantitative assessment, such as the Marshall scoring system, which is prone to subjectivity and human errors. This work explores the predictive power of imaging biomarkers extracted from routinely-acquired hospital admission CT scans using a state-of-the-art, deep learning TBI lesion segmentation method. We use lesion volumes and corresponding lesion statistics as inputs for an extended TBI outcome prediction model. We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers. We find that automatically extracted quantitative CT features perform similarly or better than the Marshall score in predicting unfavourable TBI outcomes. Leveraging automatic atlas alignment, we also identify frontal extra-axial lesions as important indicators of poor outcome. Our work may contribute to a better understanding of TBI, and provides new insights into how automated neuroimaging analysis can be used to improve prognostication after TBI.