Early detection of emphysema progression

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Early detection of emphysema progression. / Gorbunova, Vladlena; Jacobs, Sander S. A. M.; Lo, Pechin Chien Pau; Dirksen, Asger; Nielsen, Mads; Bab-Hadiashar, Alireza; de Bruijne, Marleen.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II. red. / Tianzi Jiang; Nassir Navab; Josien P. W. Pluim; Max A. Viergever. Bind Part II Springer, 2010. s. 193-200 (Lecture notes in computer science; Nr. 6362).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Gorbunova, V, Jacobs, SSAM, Lo, PCP, Dirksen, A, Nielsen, M, Bab-Hadiashar, A & de Bruijne, M 2010, Early detection of emphysema progression. i T Jiang, N Navab, JPW Pluim & MA Viergever (red), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II. bind Part II, Springer, Lecture notes in computer science, nr. 6362, s. 193-200, 13th International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, Kina, 20/09/2010. https://doi.org/10.1007/978-3-642-15745-5_24

APA

Gorbunova, V., Jacobs, S. S. A. M., Lo, P. C. P., Dirksen, A., Nielsen, M., Bab-Hadiashar, A., & de Bruijne, M. (2010). Early detection of emphysema progression. I T. Jiang, N. Navab, J. P. W. Pluim, & M. A. Viergever (red.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II (Bind Part II, s. 193-200). Springer. Lecture notes in computer science, Nr. 6362 https://doi.org/10.1007/978-3-642-15745-5_24

Vancouver

Gorbunova V, Jacobs SSAM, Lo PCP, Dirksen A, Nielsen M, Bab-Hadiashar A o.a. Early detection of emphysema progression. I Jiang T, Navab N, Pluim JPW, Viergever MA, red., Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II. Bind Part II. Springer. 2010. s. 193-200. (Lecture notes in computer science; Nr. 6362). https://doi.org/10.1007/978-3-642-15745-5_24

Author

Gorbunova, Vladlena ; Jacobs, Sander S. A. M. ; Lo, Pechin Chien Pau ; Dirksen, Asger ; Nielsen, Mads ; Bab-Hadiashar, Alireza ; de Bruijne, Marleen. / Early detection of emphysema progression. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part II. red. / Tianzi Jiang ; Nassir Navab ; Josien P. W. Pluim ; Max A. Viergever. Bind Part II Springer, 2010. s. 193-200 (Lecture notes in computer science; Nr. 6362).

Bibtex

@inproceedings{e603a990a18b11df928f000ea68e967b,
title = "Early detection of emphysema progression",
abstract = "Emphysema is one of the most widespread diseases in subjects with smoking history. The gold standard method for estimating the severity of emphysema is a lung function test, such as forced expiratory volume in first second (FEV1). However, several clinical studies showed that chest CT scans offer more sensitive estimates of emphysema progression. The standard CT densitometric score of emphysema is the relative area of voxels below a threshold (RA). The RA score is a global measurement and reflects the overall emphysema progression. In this work, we propose a framework for estimation of local emphysema progression from longitudinal chest CT scans. First, images are registered to a common system of coordinates and then local image dissimilarities are computed in corresponding anatomical locations. Finally, the obtained dissimilarity representation is converted into a single emphysema progression score. We applied the proposed algorithm on 27 patients with severe emphysema with CT scans acquired five time points, at baseline, after 3, after 12, after 21 and after 24 or 30 months. The results showed consistent emphysema progression with time and the overall progression score correlates significantly with the increase in RA score.",
author = "Vladlena Gorbunova and Jacobs, {Sander S. A. M.} and Lo, {Pechin Chien Pau} and Asger Dirksen and Mads Nielsen and Alireza Bab-Hadiashar and {de Bruijne}, Marleen",
year = "2010",
doi = "10.1007/978-3-642-15745-5_24",
language = "English",
isbn = "978-3-642-15744-8",
volume = "Part II",
series = "Lecture notes in computer science",
publisher = "Springer",
number = "6362",
pages = "193--200",
editor = "Tianzi Jiang and Nassir Navab and Pluim, {Josien P. W.} and Viergever, {Max A.}",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010",
note = "null ; Conference date: 20-09-2010 Through 24-09-2010",

}

RIS

TY - GEN

T1 - Early detection of emphysema progression

AU - Gorbunova, Vladlena

AU - Jacobs, Sander S. A. M.

AU - Lo, Pechin Chien Pau

AU - Dirksen, Asger

AU - Nielsen, Mads

AU - Bab-Hadiashar, Alireza

AU - de Bruijne, Marleen

N1 - Conference code: 13

PY - 2010

Y1 - 2010

N2 - Emphysema is one of the most widespread diseases in subjects with smoking history. The gold standard method for estimating the severity of emphysema is a lung function test, such as forced expiratory volume in first second (FEV1). However, several clinical studies showed that chest CT scans offer more sensitive estimates of emphysema progression. The standard CT densitometric score of emphysema is the relative area of voxels below a threshold (RA). The RA score is a global measurement and reflects the overall emphysema progression. In this work, we propose a framework for estimation of local emphysema progression from longitudinal chest CT scans. First, images are registered to a common system of coordinates and then local image dissimilarities are computed in corresponding anatomical locations. Finally, the obtained dissimilarity representation is converted into a single emphysema progression score. We applied the proposed algorithm on 27 patients with severe emphysema with CT scans acquired five time points, at baseline, after 3, after 12, after 21 and after 24 or 30 months. The results showed consistent emphysema progression with time and the overall progression score correlates significantly with the increase in RA score.

AB - Emphysema is one of the most widespread diseases in subjects with smoking history. The gold standard method for estimating the severity of emphysema is a lung function test, such as forced expiratory volume in first second (FEV1). However, several clinical studies showed that chest CT scans offer more sensitive estimates of emphysema progression. The standard CT densitometric score of emphysema is the relative area of voxels below a threshold (RA). The RA score is a global measurement and reflects the overall emphysema progression. In this work, we propose a framework for estimation of local emphysema progression from longitudinal chest CT scans. First, images are registered to a common system of coordinates and then local image dissimilarities are computed in corresponding anatomical locations. Finally, the obtained dissimilarity representation is converted into a single emphysema progression score. We applied the proposed algorithm on 27 patients with severe emphysema with CT scans acquired five time points, at baseline, after 3, after 12, after 21 and after 24 or 30 months. The results showed consistent emphysema progression with time and the overall progression score correlates significantly with the increase in RA score.

U2 - 10.1007/978-3-642-15745-5_24

DO - 10.1007/978-3-642-15745-5_24

M3 - Article in proceedings

SN - 978-3-642-15744-8

VL - Part II

T3 - Lecture notes in computer science

SP - 193

EP - 200

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010

A2 - Jiang, Tianzi

A2 - Navab, Nassir

A2 - Pluim, Josien P. W.

A2 - Viergever, Max A.

PB - Springer

Y2 - 20 September 2010 through 24 September 2010

ER -

ID: 21235859