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Cited by 146Colorectal Cancer Screening and DetectionAI in cancer detectionMedical Image Segmentation Techniques

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Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps

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Fernández-Esparrach, Glòria, Bernal, Jorge, López-Cerón, Maria, Córdova, Henry, Sánchez-Montes, Cristina, Rodríguez de Miguel, Cristina, Sánchez, Francisco·2016·Endoscopy
DOI 10.1055/s-0042-108434
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Abstract

BACKGROUND AND AIMS: Polyp miss-rate is a drawback of colonoscopy that increases significantly for small polyps. We explored the efficacy of an automatic computer-vision method for polyp detection. METHODS: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps that represent the likelihood of the presence of a polyp. RESULTS: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. The mean of the maximum values on the energy map was higher for frames with polyps than without (P < 0.001). Performance improved in high quality frames (AUC = 0.79 [95 %CI 0.70 - 0.87] vs. 0.75 [95 %CI 0.66 - 0.83]). With 3.75 set as the maximum threshold value, sensitivity and specificity for the detection of polyps were 70.4 % (95 %CI 60.3 % - 80.8 %) and 72.4 % (95 %CI 61.6 % - 84.6 %), respectively. CONCLUSION: Energy maps performed well for colonic polyp detection, indicating their potential applicability in clinical practice.

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Metadata

Authors
Fernández-Esparrach, G., Bernal, J., López-Cerón, M., Córdova, H., Sánchez-Montes, C., Rodríguez de Miguel, C., & Sánchez, F.
Affiliations
  • Endoscopy Unit, Gastroenterology Department, Hospital Clínic, IDIBAPS, CIBEREHD, University of Barcelona, Barcelona, Spain
  • Universitat Autònoma de Barcelona (ES)
  • Hospital Clínic de Barcelona (ES)
ORCID
Venue
Endoscopy
Publisher
Georg Thieme Verlag KG
Published
2016-06-10
Volume
48
Issue
09
Pages
837-842
ISSN
  • 0013-726X (ISSN-L)
  • 1438-8812
DOI
10.1055/s-0042-108434
OpenAlex
W2410282112
Cited by
146
References
0
Citations by year
2026: 1 · 2025: 17 · 2024: 8 · 2023: 18 · 2022: 21 · 2021: 33 · 2020: 24 · 2019: 13 · 2018: 6 · 2017: 5
FWCI
4.31
Type
journal article
Language
en
Open access
Licence
public-domain
Locations
  • Endoscopy (publishedVersion)
  • PubMed (publishedVersion)
  • Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, public-domain)
Funders
Generalitat de Catalunya (2014SGR135, 2014-SGR-1470)
Topics
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
colorectal polyp detection, energy map, computer-aided detection, colonoscopy, polyp miss rate, small polyps
MeSH terms
Colonic Polyps, Colonoscopy, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Video Recording

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