Improved detection of air trapping on expiratory computed tomography using deep learning
Background Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
March 24, 2021
|
| In: |
PLOS ONE
Year: 2021, Volume: 16, Issue: 3, Pages: 1-17 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0248902 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1371/journal.pone.0248902 Verlag, lizenzpflichtig, Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248902 
|
| Author Notes: | Sundaresh Ram, Benjamin A. Hoff, Alexander J. Bell, Stefanie Galban, Aleksa B. Fortuna, Oliver Weinheimer, Mark O. Wielpütz, Terry E. Robinson, Beverley Newman, Dharshan Vummidi, Aamer Chughtai, Ella A. Kazerooni, Timothy D. Johnson, MeiLan K. Han, Charles R. Hatt, Craig J. Galban |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1757500782 | ||
| 003 | DE-627 | ||
| 005 | 20230426112706.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210507s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1371/journal.pone.0248902 |2 doi | |
| 035 | |a (DE-627)1757500782 | ||
| 035 | |a (DE-599)KXP1757500782 | ||
| 035 | |a (OCoLC)1341408631 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 33 |2 sdnb | ||
| 100 | 1 | |a Ram, Sundaresh |e VerfasserIn |0 (DE-588)1233380397 |0 (DE-627)1757719415 |4 aut | |
| 245 | 1 | 0 | |a Improved detection of air trapping on expiratory computed tomography using deep learning |c Sundaresh Ram, Benjamin A. Hoff, Alexander J. Bell, Stefanie Galban, Aleksa B. Fortuna, Oliver Weinheimer, Mark O. Wielpütz, Terry E. Robinson, Beverley Newman, Dharshan Vummidi, Aamer Chughtai, Ella A. Kazerooni, Timothy D. Johnson, MeiLan K. Han, Charles R. Hatt, Craig J. Galban |
| 264 | 1 | |c March 24, 2021 | |
| 300 | |a 17 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Gesehen am 12.05.2021 | ||
| 520 | |a Background Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients. | ||
| 650 | 4 | |a Computed axial tomography | |
| 650 | 4 | |a Cystic fibrosis | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Imaging techniques | |
| 650 | 4 | |a Mucus | |
| 650 | 4 | |a Pulmonary function | |
| 650 | 4 | |a Pulmonary imaging | |
| 650 | 4 | |a Radiologists | |
| 700 | 1 | |a Hoff, Benjamin A. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Bell, Alexander J. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Galban, Stefanie |e VerfasserIn |4 aut | |
| 700 | 1 | |a Fortuna, Aleksa B. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Weinheimer, Oliver |d 1973- |e VerfasserIn |0 (DE-588)133803473 |0 (DE-627)556388585 |0 (DE-576)30011558X |4 aut | |
| 700 | 1 | |a Wielpütz, Mark Oliver |d 1982- |e VerfasserIn |0 (DE-588)139999752 |0 (DE-627)614754682 |0 (DE-576)314127046 |4 aut | |
| 700 | 1 | |a Robinson, Terry E. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Newman, Beverley |e VerfasserIn |4 aut | |
| 700 | 1 | |a Vummidi, Dharshan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Chughtai, Aamer |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kazerooni, Ella A. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Johnson, Timothy D. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Han, MeiLan K. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Hatt, Charles R. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Galban, Craig J. |e VerfasserIn |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t PLOS ONE |d San Francisco, California, US : PLOS, 2006 |g 16(2021), 3, Artikel-ID e0248902, Seite 1-17 |h Online-Ressource |w (DE-627)523574592 |w (DE-600)2267670-3 |w (DE-576)281331979 |x 1932-6203 |7 nnas |a Improved detection of air trapping on expiratory computed tomography using deep learning |
| 773 | 1 | 8 | |g volume:16 |g year:2021 |g number:3 |g elocationid:e0248902 |g pages:1-17 |g extent:17 |a Improved detection of air trapping on expiratory computed tomography using deep learning |
| 856 | 4 | 0 | |u https://doi.org/10.1371/journal.pone.0248902 |x Verlag |x Resolving-System |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248902 |x Verlag |z lizenzpflichtig |3 Volltext |
| 951 | |a AR | ||
| 992 | |a 20210507 | ||
| 993 | |a Article | ||
| 994 | |a 2021 | ||
| 998 | |g 139999752 |a Wielpütz, Mark Oliver |m 139999752:Wielpütz, Mark Oliver |d 910000 |d 911400 |d 50000 |e 910000PW139999752 |e 911400PW139999752 |e 50000PW139999752 |k 0/910000/ |k 1/910000/911400/ |k 0/50000/ |p 7 | ||
| 998 | |g 133803473 |a Weinheimer, Oliver |m 133803473:Weinheimer, Oliver |d 910000 |d 911400 |e 910000PW133803473 |e 911400PW133803473 |k 0/910000/ |k 1/910000/911400/ |p 6 | ||
| 999 | |a KXP-PPN1757500782 |e 3925055126 | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"physDesc":[{"extent":"17 S."}],"relHost":[{"type":{"media":"Online-Ressource","bibl":"periodical"},"disp":"Improved detection of air trapping on expiratory computed tomography using deep learningPLOS ONE","language":["eng"],"title":[{"title":"PLOS ONE","title_sort":"PLOS ONE"}],"corporate":[{"role":"isb","display":"Public Library of Science"}],"part":{"extent":"17","volume":"16","year":"2021","issue":"3","pages":"1-17","text":"16(2021), 3, Artikel-ID e0248902, Seite 1-17"},"id":{"zdb":["2267670-3"],"issn":["1932-6203"],"eki":["523574592"]},"pubHistory":["1.2006 -"],"name":{"displayForm":["Public Library of Science"]},"recId":"523574592","physDesc":[{"extent":"Online-Ressource"}],"note":["Schreibweise des Titels bis 2012: PLoS ONE","Gesehen am 20.03.19"],"origin":[{"publisher":"PLOS ; PLoS","dateIssuedDisp":"2006-","publisherPlace":"San Francisco, California, US ; Lawrence, Kan.","dateIssuedKey":"2006"}]}],"person":[{"given":"Sundaresh","family":"Ram","display":"Ram, Sundaresh","role":"aut"},{"display":"Hoff, Benjamin A.","role":"aut","family":"Hoff","given":"Benjamin A."},{"role":"aut","display":"Bell, Alexander J.","given":"Alexander J.","family":"Bell"},{"given":"Stefanie","family":"Galban","role":"aut","display":"Galban, Stefanie"},{"family":"Fortuna","given":"Aleksa B.","display":"Fortuna, Aleksa B.","role":"aut"},{"role":"aut","display":"Weinheimer, Oliver","family":"Weinheimer","given":"Oliver"},{"family":"Wielpütz","given":"Mark Oliver","role":"aut","display":"Wielpütz, Mark Oliver"},{"role":"aut","display":"Robinson, Terry E.","family":"Robinson","given":"Terry E."},{"role":"aut","display":"Newman, Beverley","family":"Newman","given":"Beverley"},{"given":"Dharshan","family":"Vummidi","display":"Vummidi, Dharshan","role":"aut"},{"family":"Chughtai","given":"Aamer","display":"Chughtai, Aamer","role":"aut"},{"given":"Ella A.","family":"Kazerooni","display":"Kazerooni, Ella A.","role":"aut"},{"given":"Timothy D.","family":"Johnson","display":"Johnson, Timothy D.","role":"aut"},{"family":"Han","given":"MeiLan K.","display":"Han, MeiLan K.","role":"aut"},{"family":"Hatt","given":"Charles R.","display":"Hatt, Charles R.","role":"aut"},{"role":"aut","display":"Galban, Craig J.","given":"Craig J.","family":"Galban"}],"title":[{"title":"Improved detection of air trapping on expiratory computed tomography using deep learning","title_sort":"Improved detection of air trapping on expiratory computed tomography using deep learning"}],"origin":[{"dateIssuedDisp":"March 24, 2021","dateIssuedKey":"2021"}],"note":["Gesehen am 12.05.2021"],"id":{"doi":["10.1371/journal.pone.0248902"],"eki":["1757500782"]},"recId":"1757500782","language":["eng"],"name":{"displayForm":["Sundaresh Ram, Benjamin A. Hoff, Alexander J. Bell, Stefanie Galban, Aleksa B. Fortuna, Oliver Weinheimer, Mark O. Wielpütz, Terry E. Robinson, Beverley Newman, Dharshan Vummidi, Aamer Chughtai, Ella A. Kazerooni, Timothy D. Johnson, MeiLan K. Han, Charles R. Hatt, Craig J. Galban"]},"type":{"media":"Online-Ressource","bibl":"article-journal"}} | ||
| SRT | |a RAMSUNDAREIMPROVEDDE2420 | ||