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 - der Sommen, Fons van
AU - de With, Peter H. N.
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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 endoscopy-driven 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.
AB - 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 endoscopy-driven 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.
KW - Barrett’s Esophagus
KW - Deep learning
KW - NBI
KW - Video analysis
UR - http://www.scopus.com/inward/record.url?scp=85140465782&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-17979-2_9
DO - https://doi.org/10.1007/978-3-031-17979-2_9
M3 - Conference contribution
SN - 9783031179785
VL - 13581 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 89
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 Science and Business Media Deutschland GmbH
T2 - 1st International Workshop on Cancer Prevention through Early Detection, CaPTion 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -