Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
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.
Date Issued
2022-10-06
Date Acceptance
2022-10-01
Citation
2022, pp.135-146
ISBN
9783031178986
ISSN
0302-9743
Publisher
Springer Nature Switzerland
Start Page
135
End Page
146
Copyright Statement
Copyright © 2022 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/[insert DOI]
Identifier
https://link.springer.com/book/10.1007/978-3-031-17899-3
Source
5th International Workshop, MLCN 2022
Publication Status
Published
Start Date
2022-09-18
Finish Date
2022-09-18
Coverage Spatial
Singapore
Date Publish Online
2022-10-06