Automatic quantification of CT images for traumatic brain injury

Juha Koikkalainen, Jyrki Lötjönen, Christian Ledig, Daniel Rueckert, Olli Tenovuo, David Menon

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationIEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages125-128
ISBN (Electronic)978-1-4673-1961-4
ISBN (Print)978-1-4673-1959-1
DOIs
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
Event11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2014 - Peking, China
Duration: 29 Apr 20142 May 2014

Conference

Conference11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2014
Abbreviated titleISBI 2014
CountryChina
CityPeking
Period29/04/142/05/14

Fingerprint

Tomography
Pathology
Traumatic Brain Injury
Health

Keywords

  • traumatic brain injury
  • CT
  • registration
  • segmentation
  • classification
  • prognosis
  • multi-template

Cite this

Koikkalainen, J., Lötjönen, J., Ledig, C., Rueckert, D., Tenovuo, O., & Menon, D. (2014). Automatic quantification of CT images for traumatic brain injury. In IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 125-128). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ISBI.2014.6867825
Koikkalainen, Juha ; Lötjönen, Jyrki ; Ledig, Christian ; Rueckert, Daniel ; Tenovuo, Olli ; Menon, David. / Automatic quantification of CT images for traumatic brain injury. IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. pp. 125-128
@inproceedings{b393475cb6c24adcad01eb93e93fa2bb,
title = "Automatic quantification of CT images for traumatic brain injury",
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{\%}.",
keywords = "traumatic brain injury, CT, registration, segmentation, classification, prognosis, multi-template",
author = "Juha Koikkalainen and Jyrki L{\"o}tj{\"o}nen and Christian Ledig and Daniel Rueckert and Olli Tenovuo and David Menon",
note = "Project code: 70554 Project code: 71992",
year = "2014",
doi = "10.1109/ISBI.2014.6867825",
language = "English",
isbn = "978-1-4673-1959-1",
pages = "125--128",
booktitle = "IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
address = "United States",

}

Koikkalainen, J, Lötjönen, J, Ledig, C, Rueckert, D, Tenovuo, O & Menon, D 2014, Automatic quantification of CT images for traumatic brain injury. in IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronic Engineers IEEE, pp. 125-128, 11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2014, Peking, China, 29/04/14. https://doi.org/10.1109/ISBI.2014.6867825

Automatic quantification of CT images for traumatic brain injury. / Koikkalainen, Juha; Lötjönen, Jyrki; Ledig, Christian; Rueckert, Daniel; Tenovuo, Olli; Menon, David.

IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. p. 125-128.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Automatic quantification of CT images for traumatic brain injury

AU - Koikkalainen, Juha

AU - Lötjönen, Jyrki

AU - Ledig, Christian

AU - Rueckert, Daniel

AU - Tenovuo, Olli

AU - Menon, David

N1 - Project code: 70554 Project code: 71992

PY - 2014

Y1 - 2014

N2 - 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%.

AB - 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%.

KW - traumatic brain injury

KW - CT

KW - registration

KW - segmentation

KW - classification

KW - prognosis

KW - multi-template

U2 - 10.1109/ISBI.2014.6867825

DO - 10.1109/ISBI.2014.6867825

M3 - Conference article in proceedings

SN - 978-1-4673-1959-1

SP - 125

EP - 128

BT - IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

PB - Institute of Electrical and Electronic Engineers IEEE

ER -

Koikkalainen J, Lötjönen J, Ledig C, Rueckert D, Tenovuo O, Menon D. Automatic quantification of CT images for traumatic brain injury. In IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronic Engineers IEEE. 2014. p. 125-128 https://doi.org/10.1109/ISBI.2014.6867825