Abstract
Traumatic brain injury (TBI) is a major health problem
and the most common cause of permanent disability in
people under the age of 40 years. In this paper, we
present a fully automatic framework for the analysis of
acute computed tomography (CT) images in TBI. Different
pathologies common in TBI are quantified and all the
information is combined for clinical outcome prediction
in individual patients. We propose a multi-template
approach for the registration of CT data, which improves
the robustness and accuracy of spatial normalization.
This is especially important for noisy CT data and TBI
images with large areas of pathology. The tissue
segmentation methods we use have been optimized to deal
with these challenges. The methods we describe have been
evaluated on acute CTs from 104 TBI patients. We
demonstrate on this dataset that the prediction of
dichotomized favorable or unfavorable outcome can be made
with an accuracy of 79%.
Original language | English |
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Title of host publication | IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 125-128 |
ISBN (Electronic) | 978-1-4673-1961-4 |
ISBN (Print) | 978-1-4673-1959-1 |
DOIs | |
Publication status | Published - 2014 |
MoE publication type | A4 Article in a conference publication |
Event | 11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2014 - Peking, China Duration: 29 Apr 2014 → 2 May 2014 |
Conference
Conference | 11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2014 |
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Abbreviated title | ISBI 2014 |
Country/Territory | China |
City | Peking |
Period | 29/04/14 → 2/05/14 |
Keywords
- traumatic brain injury
- CT
- registration
- segmentation
- classification
- prognosis
- multi-template