The field effect in Barrett's Esophagus: A macroscopic view using white light endoscopy and deep learning

Levi Verhage, Joost van der Putten, Fons van der Sommen, Jeroen De Groof, Maarten Struyvenberg, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


Over the past few decades, primarily developed countries witnessed an increased incidence of esophageal adenocarcinoma (EAC). Screening and surveillance of Barrett's esophagus (BE), which is known to augment the probability of developing EAC, can significantly improve survival rates. This is because early-stage dysplasia in BE can be treated effectively, while each subsequent stage complicates successful treatment and seriously reduces survival rates. This study proposes a convolutional neural network-based algorithm, which classifies images of BE visualized with White Light Endoscopy (WLE) as either dysplastic or non-dysplastic. To this end, we use merely pixels surrounding the dysplastic region, while excluding the pixels covering the dysplastic region itself. The phenomenon where the diagnosis of a patient can be determined from tissue other than the clearly observable diseased area, is termed the field effect. With its potential to identify missed lesions, it may prove to be a helpful innovation in the screening and surveillance process of BE. A statistical significant difference test indicates the presence of the field effect in WLE, when comparing the distribution of the algorithm classifications of unseen data and the distribution obtained by a random classification.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
Number of pages7
ISBN (Electronic) 9781510633964
ISBN (Print)9781510633957
Publication statusPublished - 2020
EventSPIE Medical Imaging 2020 - Houston, United States
Duration: 15 Feb 202020 Feb 2020

Publication series

NameProceedings of SPIE
ISSN (Print)1605-7422


ConferenceSPIE Medical Imaging 2020
Country/TerritoryUnited States


  • barrett's esophagus
  • convolutional neural networks
  • deep learning
  • field effect


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