Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Hinrich B Winther
- Marcel Gutberlet
- Christian Hundt
- Till F Kaireit
- Tawfik Moher Alsady
- Bertil Schmidt
- Frank Wacker
- Yanping Sun
- Sabine Dettmer
- Sabine K Maschke
- Jan B Hinrichs
- Sachin Jambawalikar
- Martin R Prince
- R Graham Barr
- Jens Vogel-Claussen
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000506450300023&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1002/jmri.26853
- eISSN
- 1522-2586
- Externe Identifier
- Clarivate Analytics Document Solution ID: KB4EG
- PubMed Identifier: 31276264
- ISSN
- 1053-1807
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- JOURNAL OF MAGNETIC RESONANCE IMAGING
- Schlüsselwörter
- lung perfusion
- 4D DCE
- COPD
- deep semantic segmentation
- nonparametric deconvolution
- Paginierung
- 571 - 579
- Datum der Veröffentlichung
- 2020
- Status
- Published
- Titel
- Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study
- Sub types
- Article
- Ausgabe der Zeitschrift
- 51
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Abstract
- <jats:sec><jats:title>Background</jats:title><jats:p>Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality. Identification of imaging biomarkers for phenotyping is necessary for future treatment and therapy monitoring. However, translation of visual analytic pipelines into clinics or their use in large‐scale studies is significantly slowed by time‐consuming postprocessing steps.</jats:p></jats:sec><jats:sec><jats:title>Purpose</jats:title><jats:p>To implement an automated tool chain for regional quantification of pulmonary microvascular blood flow in order to reduce analysis time and user variability.</jats:p></jats:sec><jats:sec><jats:title>Study Type</jats:title><jats:p>Prospective.</jats:p></jats:sec><jats:sec><jats:title>Population</jats:title><jats:p>In all, 90 MRI scans of 63 patients, of which 31 had a COPD with a mean Global Initiative for Chronic Obstructive Lung Disease status of 1.9 ± 0.64 (μ ± σ).</jats:p></jats:sec><jats:sec><jats:title>Field Strength/Sequence</jats:title><jats:p>1.5T dynamic gadolinium‐enhanced MRI measurement using 4D dynamic contrast material‐enhanced (DCE) time‐resolved angiography acquired in a single breath‐hold in inspiration. [Correction added on August 20, 2019, after first online publication: The field strength in the preceding sentence was corrected.]</jats:p></jats:sec><jats:sec><jats:title>Assessment</jats:title><jats:p>We built a 3D convolutional neural network for semantic segmentation using 29 manually segmented perfusion maps. All five lobes of the lung are denoted, including the middle lobe. Evaluation was performed on 61 independent cases from two sites of the Multi‐Ethnic Study of Arteriosclerosis (MESA)‐COPD study. We publish our implementation of a model‐free deconvolution filter according to Sourbron et al for 4D DCE MRI scans as open source.</jats:p></jats:sec><jats:sec><jats:title>Statistical Test</jats:title><jats:p>Cross‐validation 29/61 (# training / # testing), intraclass correlation coefficient (ICC), Spearman ρ, Pearson <jats:italic>r</jats:italic>, Sørensen–Dice coefficient, and overlap.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Segmentations and derived clinical parameters were processed in ~90 seconds per case on a Xeon E5‐2637v4 workstation with Tesla P40 GPUs. Clinical parameters and predicted segmentations exhibit high concordance with the ground truth regarding median perfusion for all lobes with an ICC of 0.99 and a Sørensen–Dice coefficient of 93.4 ± 2.8 (μ ± σ).</jats:p></jats:sec><jats:sec><jats:title>Data Conclusion</jats:title><jats:p>We present a robust end‐to‐end pipeline that allows for the extraction of perfusion‐based biomarkers for all lung lobes in 4D DCE MRI scans by combining model‐free deconvolution with deep learning.</jats:p><jats:p><jats:bold>Level of Evidence:</jats:bold> 3</jats:p><jats:p><jats:bold>Technical Efficacy:</jats:bold> Stage 2</jats:p><jats:p>J. Magn. Reson. Imaging 2020;51:571–579.</jats:p></jats:sec>
- Autoren
- Hinrich B Winther
- Marcel Gutberlet
- Christian Hundt
- Till F Kaireit
- Tawfik Moher Alsady
- Bertil Schmidt
- Frank Wacker
- Yanping Sun
- Sabine Dettmer
- Sabine K Maschke
- Jan B Hinrichs
- Sachin Jambawalikar
- Martin R Prince
- R Graham Barr
- Jens Vogel‐Claussen
- DOI
- 10.1002/jmri.26853
- eISSN
- 1522-2586
- ISSN
- 1053-1807
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- Journal of Magnetic Resonance Imaging
- Sprache
- en
- Online publication date
- 2019
- Paginierung
- 571 - 579
- Datum der Veröffentlichung
- 2020
- Status
- Published
- Herausgeber
- Wiley
- Herausgeber URL
- http://dx.doi.org/10.1002/jmri.26853
- Datum der Datenerfassung
- 2023
- Titel
- Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi‐ethnic study of atherosclerosis COPD study
- Ausgabe der Zeitschrift
- 51
Datenquelle: Crossref
- Abstract
- <h4>Background</h4>Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality. Identification of imaging biomarkers for phenotyping is necessary for future treatment and therapy monitoring. However, translation of visual analytic pipelines into clinics or their use in large-scale studies is significantly slowed by time-consuming postprocessing steps.<h4>Purpose</h4>To implement an automated tool chain for regional quantification of pulmonary microvascular blood flow in order to reduce analysis time and user variability.<h4>Study type</h4>Prospective.<h4>Population</h4>In all, 90 MRI scans of 63 patients, of which 31 had a COPD with a mean Global Initiative for Chronic Obstructive Lung Disease status of 1.9 ± 0.64 (μ ± σ).<h4>Field strength/sequence</h4>1.5T dynamic gadolinium-enhanced MRI measurement using 4D dynamic contrast material-enhanced (DCE) time-resolved angiography acquired in a single breath-hold in inspiration. [Correction added on August 20, 2019, after first online publication: The field strength in the preceding sentence was corrected.] ASSESSMENT: We built a 3D convolutional neural network for semantic segmentation using 29 manually segmented perfusion maps. All five lobes of the lung are denoted, including the middle lobe. Evaluation was performed on 61 independent cases from two sites of the Multi-Ethnic Study of Arteriosclerosis (MESA)-COPD study. We publish our implementation of a model-free deconvolution filter according to Sourbron et al for 4D DCE MRI scans as open source.<h4>Statistical test</h4>Cross-validation 29/61 (# training / # testing), intraclass correlation coefficient (ICC), Spearman ρ, Pearson r, Sørensen-Dice coefficient, and overlap.<h4>Results</h4>Segmentations and derived clinical parameters were processed in ~90 seconds per case on a Xeon E5-2637v4 workstation with Tesla P40 GPUs. Clinical parameters and predicted segmentations exhibit high concordance with the ground truth regarding median perfusion for all lobes with an ICC of 0.99 and a Sørensen-Dice coefficient of 93.4 ± 2.8 (μ ± σ).<h4>Data conclusion</h4>We present a robust end-to-end pipeline that allows for the extraction of perfusion-based biomarkers for all lung lobes in 4D DCE MRI scans by combining model-free deconvolution with deep learning.<h4>Level of evidence</h4>3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:571-579.
- Addresses
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany.
- Autoren
- Hinrich B Winther
- Marcel Gutberlet
- Christian Hundt
- Till F Kaireit
- Tawfik Moher Alsady
- Bertil Schmidt
- Frank Wacker
- Yanping Sun
- Sabine Dettmer
- Sabine K Maschke
- Jan B Hinrichs
- Sachin Jambawalikar
- Martin R Prince
- R Graham Barr
- Jens Vogel-Claussen
- DOI
- 10.1002/jmri.26853
- eISSN
- 1522-2586
- Externe Identifier
- PubMed Identifier: 31276264
- Funding acknowledgements
- NHLBI NIH HHS: N01-HC-95160
- NHLBI NIH HHS: N01-HC-95161
- National Heart, Lung, and Blood Institute: N01‐HC‐95165
- National Heart, Lung, and Blood Institute: N01‐HC‐95167
- NHLBI NIH HHS: N01-HC-95168
- NHLBI NIH HHS: N01-HC-95169
- National Heart, Lung, and Blood Institute: N01‐HC‐95159
- National Heart, Lung, and Blood Institute: N01‐HC‐95166
- National Heart, Lung, and Blood Institute: N01‐HC‐95168
- National Heart, Lung, and Blood Institute: HHSN268201500003I
- National Heart, Lung, and Blood Institute: N01‐HC‐95162
- National Heart, Lung, and Blood Institute: N01‐HC‐95163
- NHLBI NIH HHS: R01-HL093081
- National Heart, Lung, and Blood Institute: R01‐HL093081
- National Center for Advancing Translational Sciences: UL1‐TR‐001420
- NHLBI NIH HHS: N01-HC-95162
- NHLBI NIH HHS: N01-HC-95166
- National Center for Advancing Translational Sciences: UL1‐TR‐000040
- NHLBI NIH HHS: N01-HC-95164
- National Heart, Lung, and Blood Institute: N01‐HC‐95161
- National Heart, Lung, and Blood Institute: R01‐HL077612
- NCATS NIH HHS: UL1-TR-000040
- NCATS NIH HHS: UL1-TR-001420
- NHLBI NIH HHS: N01-HC-95163
- NHLBI NIH HHS: N01-HC-95165
- NCATS NIH HHS: UL1-TR-001079
- NHLBI NIH HHS: HHSN268201500003I
- NHLBI NIH HHS: N01-HC-95159
- NHLBI NIH HHS: N01-HC-95167
- National Heart, Lung, and Blood Institute: N01‐HC‐95160
- National Heart, Lung, and Blood Institute: N01‐HC‐95164
- National Heart, Lung, and Blood Institute: N01‐HC‐95169
- NHLBI NIH HHS: R01-HL077612
- National Center for Advancing Translational Sciences: UL1‐TR‐001079
- Deutsche Forschungsgemeinschaft: WI 4739/1‐1
- Open access
- false
- ISSN
- 1053-1807
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- Journal of magnetic resonance imaging : JMRI
- Schlüsselwörter
- Lung
- Humans
- Pulmonary Disease, Chronic Obstructive
- Magnetic Resonance Imaging
- Prospective Studies
- Perfusion
- Semantics
- Atherosclerosis
- Biomarkers
- Sprache
- eng
- Medium
- Print-Electronic
- Online publication date
- 2019
- Paginierung
- 571 - 579
- Datum der Veröffentlichung
- 2020
- Status
- Published
- Datum der Datenerfassung
- 2019
- Titel
- Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study.
- Sub types
- Research Support, Non-U.S. Gov't
- Journal Article
- Research Support, N.I.H., Extramural
- Ausgabe der Zeitschrift
- 51
Datenquelle: Europe PubMed Central
- Abstract
- BACKGROUND: Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality. Identification of imaging biomarkers for phenotyping is necessary for future treatment and therapy monitoring. However, translation of visual analytic pipelines into clinics or their use in large-scale studies is significantly slowed by time-consuming postprocessing steps. PURPOSE: To implement an automated tool chain for regional quantification of pulmonary microvascular blood flow in order to reduce analysis time and user variability. STUDY TYPE: Prospective. POPULATION: In all, 90 MRI scans of 63 patients, of which 31 had a COPD with a mean Global Initiative for Chronic Obstructive Lung Disease status of 1.9 ± 0.64 (μ ± σ). FIELD STRENGTH/SEQUENCE: 1.5T dynamic gadolinium-enhanced MRI measurement using 4D dynamic contrast material-enhanced (DCE) time-resolved angiography acquired in a single breath-hold in inspiration. [Correction added on August 20, 2019, after first online publication: The field strength in the preceding sentence was corrected.] ASSESSMENT: We built a 3D convolutional neural network for semantic segmentation using 29 manually segmented perfusion maps. All five lobes of the lung are denoted, including the middle lobe. Evaluation was performed on 61 independent cases from two sites of the Multi-Ethnic Study of Arteriosclerosis (MESA)-COPD study. We publish our implementation of a model-free deconvolution filter according to Sourbron et al for 4D DCE MRI scans as open source. STATISTICAL TEST: Cross-validation 29/61 (# training / # testing), intraclass correlation coefficient (ICC), Spearman ρ, Pearson r, Sørensen-Dice coefficient, and overlap. RESULTS: Segmentations and derived clinical parameters were processed in ~90 seconds per case on a Xeon E5-2637v4 workstation with Tesla P40 GPUs. Clinical parameters and predicted segmentations exhibit high concordance with the ground truth regarding median perfusion for all lobes with an ICC of 0.99 and a Sørensen-Dice coefficient of 93.4 ± 2.8 (μ ± σ). DATA CONCLUSION: We present a robust end-to-end pipeline that allows for the extraction of perfusion-based biomarkers for all lung lobes in 4D DCE MRI scans by combining model-free deconvolution with deep learning. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:571-579.
- Date of acceptance
- 2019
- Autoren
- Hinrich B Winther
- Marcel Gutberlet
- Christian Hundt
- Till F Kaireit
- Tawfik Moher Alsady
- Bertil Schmidt
- Frank Wacker
- Yanping Sun
- Sabine Dettmer
- Sabine K Maschke
- Jan B Hinrichs
- Sachin Jambawalikar
- Martin R Prince
- R Graham Barr
- Jens Vogel-Claussen
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/31276264
- DOI
- 10.1002/jmri.26853
- eISSN
- 1522-2586
- Funding acknowledgements
- NHLBI NIH HHS: N01-HC-95159
- NHLBI NIH HHS: N01-HC-95166
- NCATS NIH HHS: UL1-TR-001420
- NHLBI NIH HHS: N01-HC-95160
- NHLBI NIH HHS: N01-HC-95167
- NCATS NIH HHS: UL1-TR-001079
- NHLBI NIH HHS: N01-HC-95168
- NHLBI NIH HHS: N01-HC-95162
- NHLBI NIH HHS: N01-HC-95164
- NHLBI NIH HHS: N01-HC-95165
- NHLBI NIH HHS: R01-HL093081
- NHLBI NIH HHS: R01-HL077612
- NHLBI NIH HHS: HHSN268201500003I
- NHLBI NIH HHS: N01-HC-95163
- NCATS NIH HHS: UL1-TR-000040
- NHLBI NIH HHS: N01-HC-95169
- NHLBI NIH HHS: N01-HC-95161
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- J Magn Reson Imaging
- Schlüsselwörter
- 4D DCE
- COPD
- deep semantic segmentation
- lung perfusion
- nonparametric deconvolution
- Atherosclerosis
- Biomarkers
- Humans
- Lung
- Magnetic Resonance Imaging
- Perfusion
- Prospective Studies
- Pulmonary Disease, Chronic Obstructive
- Semantics
- Sprache
- eng
- Country
- United States
- Paginierung
- 571 - 579
- Datum der Veröffentlichung
- 2020
- Status
- Published
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2021
- Titel
- Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study.
- Sub types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't
- Ausgabe der Zeitschrift
- 51
Datenquelle: PubMed
- Beziehungen:
- Eigentum von