A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos

Carolus H.J. Kusters; Tim G.W. Boers; Jelmer B. Jukema; Martijn R. Jong; Kiki N. Fockens; Albert J. de Groof; Jacques J. Bergman; Fons van der Sommen; Peter H.N. de With

doi:10.1007/978-3-031-17979-2_9

A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos

Carolus H.J. Kusters, Tim G.W. Boers, Jelmer B. Jukema, Martijn R. Jong, Kiki N. Fockens, Albert J. de Groof, Jacques J. Bergman, Fons van der Sommen, Peter H.N. de With

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Citaten (Scopus)

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Samenvatting

Barrett’s Esophagus (BE) is a well-known precursor for Esophageal Adenocarcinoma (EAC). Endoscopic detection and diagnosis
of early BE neoplasia is performed in two steps: primary detection of a
suspected lesion in overview and a targeted and detailed inspection of the
specific area using Narrow-Band Imaging (NBI). Despite the improved
visualization of tissue by NBI and clinical classification systems, endoscopists have difficulties with correct characterization of the imagery.
Computer-aided Diagnosis (CADx) may assist endoscopists in the classification of abnormalities in NBI imagery. We propose an endoscopydriven pre-trained deep learning-based CADx, for the characterization of NBI imagery of BE. We evaluate the performance of the algorithm on images as well as on videos, for which we use several post-hoc and real-time video analysis methods. The proposed real-time methods outperform the post-hoc methods on average by 1.2% and 2.3% for accuracy and specificity, respectively. The obtained results show promising methods towards real-time endoscopic video analysis and identifies steps for
further development.

Originele taal-2	Engels
Titel	Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings
Redacteuren	Sharib Ali, Fons van der Sommen, Maureen van Eijnatten, Iris Kolenbrander, Bartłomiej Władysław Papież, Yueming Jin
Uitgeverij	Springer
Hoofdstuk	9
Pagina's	89-98
Aantal pagina's	10
ISBN van geprinte versie	978-3-031-17978-5
DOI's	https://doi.org/10.1007/978-3-031-17979-2_9
Status	Gepubliceerd - 30 sep. 2022
Evenement	25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duur: 18 sep. 2022 → 22 sep. 2022 Congresnummer: 25

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13581 LNCS
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Congres

Congres	25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Verkorte titel	MICCAI 2022
Land/Regio	Singapore
Stad	Singapore
Periode	18/09/22 → 22/09/22

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10.1007/978-3-031-17979-2_9

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Kusters, C. H. J., Boers, T. G. W., Jukema, J. B., Jong, M. R., Fockens, K. N., de Groof, A. J., Bergman, J. J., van der Sommen, F., & de With, P. H. N. (2022). A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos. In S. Ali, F. van der Sommen, M. van Eijnatten, I. Kolenbrander, B. W. Papież, & Y. Jin (editors), Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings (blz. 89-98). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13581 LNCS). Springer. https://doi.org/10.1007/978-3-031-17979-2_9

Kusters, Carolus H.J. ; Boers, Tim G.W. ; Jukema, Jelmer B. et al. / A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos. Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings. redacteur / Sharib Ali ; Fons van der Sommen ; Maureen van Eijnatten ; Iris Kolenbrander ; Bartłomiej Władysław Papież ; Yueming Jin. Springer, 2022. blz. 89-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0c78ee28a5c54b2ea83e9177f6605a6b,

title = "A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos",

abstract = "Barrett{\textquoteright}s Esophagus (BE) is a well-known precursor for Esophageal Adenocarcinoma (EAC). Endoscopic detection and diagnosisof early BE neoplasia is performed in two steps: primary detection of asuspected lesion in overview and a targeted and detailed inspection of thespecific area using Narrow-Band Imaging (NBI). Despite the improvedvisualization of tissue by NBI and clinical classification systems, endoscopists have difficulties with correct characterization of the imagery.Computer-aided Diagnosis (CADx) may assist endoscopists in the classification of abnormalities in NBI imagery. We propose an endoscopydriven pre-trained deep learning-based CADx, for the characterization of NBI imagery of BE. We evaluate the performance of the algorithm on images as well as on videos, for which we use several post-hoc and real-time video analysis methods. The proposed real-time methods outperform the post-hoc methods on average by 1.2% and 2.3% for accuracy and specificity, respectively. The obtained results show promising methods towards real-time endoscopic video analysis and identifies steps forfurther development.",

keywords = "Barrett{\textquoteright}s Esophagus, NBI, Video analysis, Deep Learning, Deep learning",

author = "Kusters, {Carolus H.J.} and Boers, {Tim G.W.} and Jukema, {Jelmer B.} and Jong, {Martijn R.} and Fockens, {Kiki N.} and {de Groof}, {Albert J.} and Bergman, {Jacques J.} and {van der Sommen}, Fons and {de With}, {Peter H.N.}",

year = "2022",

month = sep,

day = "30",

doi = "10.1007/978-3-031-17979-2_9",

language = "English",

isbn = "978-3-031-17978-5",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "89--98",

editor = "Sharib Ali and {van der Sommen}, Fons and {van Eijnatten}, Maureen and Iris Kolenbrander and Papie{\.z}, {Bart{\l}omiej W{\l}adys{\l}aw} and Yueming Jin",

booktitle = "Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings",

address = "Germany",

note = "25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",

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Kusters, CHJ , Boers, TGW, Jukema, JB, Jong, MR, Fockens, KN, de Groof, AJ, Bergman, JJ, van der Sommen, F & de With, PHN 2022, A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos. in S Ali, F van der Sommen, M van Eijnatten, I Kolenbrander, BW Papież & Y Jin (uitgave), Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13581 LNCS, Springer, blz. 89-98, 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-17979-2_9

A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos. / Kusters, Carolus H.J.; Boers, Tim G.W.; Jukema, Jelmer B. et al.
Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings. uitgave / Sharib Ali; Fons van der Sommen; Maureen van Eijnatten; Iris Kolenbrander; Bartłomiej Władysław Papież; Yueming Jin. Springer, 2022. blz. 89-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13581 LNCS).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos

AU - Kusters, Carolus H.J.

AU - Boers, Tim G.W.

AU - Jukema, Jelmer B.

AU - Jong, Martijn R.

AU - Fockens, Kiki N.

AU - de Groof, Albert J.

AU - Bergman, Jacques J.

AU - van der Sommen, Fons

AU - de With, Peter H.N.

N1 - Conference code: 25

PY - 2022/9/30

Y1 - 2022/9/30

N2 - Barrett’s Esophagus (BE) is a well-known precursor for Esophageal Adenocarcinoma (EAC). Endoscopic detection and diagnosisof early BE neoplasia is performed in two steps: primary detection of asuspected lesion in overview and a targeted and detailed inspection of thespecific area using Narrow-Band Imaging (NBI). Despite the improvedvisualization of tissue by NBI and clinical classification systems, endoscopists have difficulties with correct characterization of the imagery.Computer-aided Diagnosis (CADx) may assist endoscopists in the classification of abnormalities in NBI imagery. We propose an endoscopydriven pre-trained deep learning-based CADx, for the characterization of NBI imagery of BE. We evaluate the performance of the algorithm on images as well as on videos, for which we use several post-hoc and real-time video analysis methods. The proposed real-time methods outperform the post-hoc methods on average by 1.2% and 2.3% for accuracy and specificity, respectively. The obtained results show promising methods towards real-time endoscopic video analysis and identifies steps forfurther development.

AB - Barrett’s Esophagus (BE) is a well-known precursor for Esophageal Adenocarcinoma (EAC). Endoscopic detection and diagnosisof early BE neoplasia is performed in two steps: primary detection of asuspected lesion in overview and a targeted and detailed inspection of thespecific area using Narrow-Band Imaging (NBI). Despite the improvedvisualization of tissue by NBI and clinical classification systems, endoscopists have difficulties with correct characterization of the imagery.Computer-aided Diagnosis (CADx) may assist endoscopists in the classification of abnormalities in NBI imagery. We propose an endoscopydriven pre-trained deep learning-based CADx, for the characterization of NBI imagery of BE. We evaluate the performance of the algorithm on images as well as on videos, for which we use several post-hoc and real-time video analysis methods. The proposed real-time methods outperform the post-hoc methods on average by 1.2% and 2.3% for accuracy and specificity, respectively. The obtained results show promising methods towards real-time endoscopic video analysis and identifies steps forfurther development.

KW - Barrett’s Esophagus

KW - NBI

KW - Video analysis

KW - Deep Learning

KW - Deep learning

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DO - 10.1007/978-3-031-17979-2_9

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SN - 978-3-031-17978-5

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 98

BT - Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings

A2 - Ali, Sharib

A2 - van der Sommen, Fons

A2 - van Eijnatten, Maureen

A2 - Kolenbrander, Iris

A2 - Papież, Bartłomiej Władysław

A2 - Jin, Yueming

PB - Springer

T2 - 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 22 September 2022

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Kusters CHJ , Boers TGW, Jukema JB, Jong MR, Fockens KN, de Groof AJ et al. A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos. In Ali S, van der Sommen F, van Eijnatten M, Kolenbrander I, Papież BW, Jin Y, redacteurs, Cancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer. 2022. blz. 89-98. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-17979-2_9

A CAD System for Real-Time Characterization of Neoplasia in Barrett's Esophagus NBI Videos

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