Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease

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Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease. / Petersen, Jens; Nielsen, Mads; Lo, Pechin Chien Pau; Nordenmark, Lars Haug; Pedersen, Jesper Johannes Holst; Wille, Mathilde Marie Winkler; Dirksen, Asger; de Bruijne, Marleen.

I: Medical Image Analysis, Bind 18, Nr. 3, 2014, s. 531-541.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Petersen, J, Nielsen, M, Lo, PCP, Nordenmark, LH, Pedersen, JJH, Wille, MMW, Dirksen, A & de Bruijne, M 2014, 'Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease', Medical Image Analysis, bind 18, nr. 3, s. 531-541. https://doi.org/10.1016/j.media.2014.02.004

APA

Petersen, J., Nielsen, M., Lo, P. C. P., Nordenmark, L. H., Pedersen, J. J. H., Wille, M. M. W., ... de Bruijne, M. (2014). Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease. Medical Image Analysis, 18(3), 531-541. https://doi.org/10.1016/j.media.2014.02.004

Vancouver

Petersen J, Nielsen M, Lo PCP, Nordenmark LH, Pedersen JJH, Wille MMW o.a. Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease. Medical Image Analysis. 2014;18(3):531-541. https://doi.org/10.1016/j.media.2014.02.004

Author

Petersen, Jens ; Nielsen, Mads ; Lo, Pechin Chien Pau ; Nordenmark, Lars Haug ; Pedersen, Jesper Johannes Holst ; Wille, Mathilde Marie Winkler ; Dirksen, Asger ; de Bruijne, Marleen. / Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease. I: Medical Image Analysis. 2014 ; Bind 18, Nr. 3. s. 531-541.

Bibtex

@article{f84967f8421f476d8895f18727b1732a,
title = "Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease",
abstract = "This paper introduces a graph construction method for multi-dimensional and multi-surface segmentation problems. Such problems can be solved by searching for the optimal separating surfaces given the space of graph columns defined by an initial coarse surface. Conventional straight graph columns are not well suited for surfaces with high curvature, we therefore propose to derive columns from properly generated, non-intersecting flow lines. This guarantees solutions that do not self-intersect. The method is applied to segment human airway walls in computed tomography images in three-dimensions. Phantom measurements show that the inner and outer radii are estimated with sub-voxel accuracy. Two-dimensional manually annotated cross-sectional images were used to compare the results with those of another recently published graph based method. The proposed approach had an average overlap of 89.3±5.8{\%}, and was on average within 0.096±0.097mm of the manually annotated surfaces, which is significantly better than what the previously published approach achieved. A medical expert visually evaluated 499 randomly extracted cross-sectional images from 499 scans and preferred the proposed approach in 68.5{\%}, the alternative approach in 11.2{\%}, and in 20.3{\%} no method was favoured. Airway abnormality measurements obtained with the method on 490 scan pairs from a lung cancer screening trial correlate significantly with lung function and are reproducible; repeat scan R(2) of measures of the airway lumen diameter and wall area percentage in the airways from generation 0 (trachea) to 5 range from 0.96 to 0.73.",
author = "Jens Petersen and Mads Nielsen and Lo, {Pechin Chien Pau} and Nordenmark, {Lars Haug} and Pedersen, {Jesper Johannes Holst} and Wille, {Mathilde Marie Winkler} and Asger Dirksen and {de Bruijne}, Marleen",
note = "Code available at https://bitbucket.org/opfront/opfront",
year = "2014",
doi = "10.1016/j.media.2014.02.004",
language = "English",
volume = "18",
pages = "531--541",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",
number = "3",

}

RIS

TY - JOUR

T1 - Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease

AU - Petersen, Jens

AU - Nielsen, Mads

AU - Lo, Pechin Chien Pau

AU - Nordenmark, Lars Haug

AU - Pedersen, Jesper Johannes Holst

AU - Wille, Mathilde Marie Winkler

AU - Dirksen, Asger

AU - de Bruijne, Marleen

N1 - Code available at https://bitbucket.org/opfront/opfront

PY - 2014

Y1 - 2014

N2 - This paper introduces a graph construction method for multi-dimensional and multi-surface segmentation problems. Such problems can be solved by searching for the optimal separating surfaces given the space of graph columns defined by an initial coarse surface. Conventional straight graph columns are not well suited for surfaces with high curvature, we therefore propose to derive columns from properly generated, non-intersecting flow lines. This guarantees solutions that do not self-intersect. The method is applied to segment human airway walls in computed tomography images in three-dimensions. Phantom measurements show that the inner and outer radii are estimated with sub-voxel accuracy. Two-dimensional manually annotated cross-sectional images were used to compare the results with those of another recently published graph based method. The proposed approach had an average overlap of 89.3±5.8%, and was on average within 0.096±0.097mm of the manually annotated surfaces, which is significantly better than what the previously published approach achieved. A medical expert visually evaluated 499 randomly extracted cross-sectional images from 499 scans and preferred the proposed approach in 68.5%, the alternative approach in 11.2%, and in 20.3% no method was favoured. Airway abnormality measurements obtained with the method on 490 scan pairs from a lung cancer screening trial correlate significantly with lung function and are reproducible; repeat scan R(2) of measures of the airway lumen diameter and wall area percentage in the airways from generation 0 (trachea) to 5 range from 0.96 to 0.73.

AB - This paper introduces a graph construction method for multi-dimensional and multi-surface segmentation problems. Such problems can be solved by searching for the optimal separating surfaces given the space of graph columns defined by an initial coarse surface. Conventional straight graph columns are not well suited for surfaces with high curvature, we therefore propose to derive columns from properly generated, non-intersecting flow lines. This guarantees solutions that do not self-intersect. The method is applied to segment human airway walls in computed tomography images in three-dimensions. Phantom measurements show that the inner and outer radii are estimated with sub-voxel accuracy. Two-dimensional manually annotated cross-sectional images were used to compare the results with those of another recently published graph based method. The proposed approach had an average overlap of 89.3±5.8%, and was on average within 0.096±0.097mm of the manually annotated surfaces, which is significantly better than what the previously published approach achieved. A medical expert visually evaluated 499 randomly extracted cross-sectional images from 499 scans and preferred the proposed approach in 68.5%, the alternative approach in 11.2%, and in 20.3% no method was favoured. Airway abnormality measurements obtained with the method on 490 scan pairs from a lung cancer screening trial correlate significantly with lung function and are reproducible; repeat scan R(2) of measures of the airway lumen diameter and wall area percentage in the airways from generation 0 (trachea) to 5 range from 0.96 to 0.73.

U2 - 10.1016/j.media.2014.02.004

DO - 10.1016/j.media.2014.02.004

M3 - Journal article

C2 - 24603047

VL - 18

SP - 531

EP - 541

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

IS - 3

ER -

ID: 112726172