Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task

  • Aims: The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task). Methods: An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D). Results: The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed. Conclusions: AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.

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Author:Alanna EbigboORCiD, Robert Mendel, Andreas Probst, Johannes Manzeneder, Luis Antonio De Souza Jr.ORCiD, João Paulo PapaORCiD, Christoph PalmORCiDGND, Helmut Messmann
Parent Title (English):Endoscopy
Publisher:Georg Thieme Verlag
Place of publication:Stuttgart
Document Type:conference proceeding (presentation, abstract)
Year of first Publication:2019
Release Date:2020/04/30
Tag:Barrett's esphagus; Deep Learning; Segmentation
GND Keyword:Speiseröhrenkrankheit; Maschinelles Lernen
First Page:6
Konferenzangabe:ESGE Days 2019
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG