Abstract
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.
This work is facilitated by data/equipment from Olympus Corp., Tokyo, Japan.
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Kusters, C.H.J. et al. (2022). A CAD System for Real-Time Characterization of Neoplasia in Barrett’s Esophagus NBI Videos. In: Ali, S., van der Sommen, F., Papież, B.W., van Eijnatten, M., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention Through Early Detection. CaPTion 2022. Lecture Notes in Computer Science, vol 13581. Springer, Cham. https://doi.org/10.1007/978-3-031-17979-2_9
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