A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Automatic Quantification of CT Images for Traumatic Brain Injury
Tekijät: Koikkalainen J, Lotjonen J, Ledig C, Rueckert D, Tenovuo O, Menon D
Toimittaja: IEEE
Konferenssin vakiintunut nimi: International Symposium on Biomedical Imaging
Julkaisuvuosi: 2014
Journal: International Symposium on Biomedical Imaging
Kokoomateoksen nimi: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
Tietokannassa oleva lehden nimi: 2014 IEEE 11TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Lehden akronyymi: I S BIOMED IMAGING
Sarjan nimi: IEEE International Symposium on Biomedical Imaging
Aloitussivu: 125
Lopetussivu: 128
Sivujen määrä: 4
eISBN: 978-1-4673-1961-4
ISSN: 1945-7928
DOI: https://doi.org/10.1109/ISBI.2014.6867825
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%.
