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CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy
Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Ass...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
© Georg Thieme Verlag KG
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035135/ https://www.ncbi.nlm.nih.gov/pubmed/32118115 http://dx.doi.org/10.1055/a-1035-9088 |
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author | Leenhardt, Romain Li, Cynthia Le Mouel, Jean-Philippe Rahmi, Gabriel Saurin, Jean Christophe Cholet, Franck Boureille, Arnaud Amiot, Xavier Delvaux, Michel Duburque, Clotilde Leandri, Chloé Gérard, Romain Lecleire, Stéphane Mesli, Farida Nion-Larmurier, Isabelle Romain, Olivier Sacher-Huvelin, Sylvie Simon-Shane, Camille Vanbiervliet, Geoffroy Marteau, Philippe Histace, Aymeric Dray, Xavier |
author_facet | Leenhardt, Romain Li, Cynthia Le Mouel, Jean-Philippe Rahmi, Gabriel Saurin, Jean Christophe Cholet, Franck Boureille, Arnaud Amiot, Xavier Delvaux, Michel Duburque, Clotilde Leandri, Chloé Gérard, Romain Lecleire, Stéphane Mesli, Farida Nion-Larmurier, Isabelle Romain, Olivier Sacher-Huvelin, Sylvie Simon-Shane, Camille Vanbiervliet, Geoffroy Marteau, Philippe Histace, Aymeric Dray, Xavier |
author_sort | Leenhardt, Romain |
collection | PubMed |
description | Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading. Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos. Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis. |
format | Online Article Text |
id | pubmed-7035135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | © Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-70351352020-03-01 CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy Leenhardt, Romain Li, Cynthia Le Mouel, Jean-Philippe Rahmi, Gabriel Saurin, Jean Christophe Cholet, Franck Boureille, Arnaud Amiot, Xavier Delvaux, Michel Duburque, Clotilde Leandri, Chloé Gérard, Romain Lecleire, Stéphane Mesli, Farida Nion-Larmurier, Isabelle Romain, Olivier Sacher-Huvelin, Sylvie Simon-Shane, Camille Vanbiervliet, Geoffroy Marteau, Philippe Histace, Aymeric Dray, Xavier Endosc Int Open Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading. Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos. Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis. © Georg Thieme Verlag KG 2020-03 2020-02-21 /pmc/articles/PMC7035135/ /pubmed/32118115 http://dx.doi.org/10.1055/a-1035-9088 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Leenhardt, Romain Li, Cynthia Le Mouel, Jean-Philippe Rahmi, Gabriel Saurin, Jean Christophe Cholet, Franck Boureille, Arnaud Amiot, Xavier Delvaux, Michel Duburque, Clotilde Leandri, Chloé Gérard, Romain Lecleire, Stéphane Mesli, Farida Nion-Larmurier, Isabelle Romain, Olivier Sacher-Huvelin, Sylvie Simon-Shane, Camille Vanbiervliet, Geoffroy Marteau, Philippe Histace, Aymeric Dray, Xavier CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
title | CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
title_full | CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
title_fullStr | CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
title_full_unstemmed | CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
title_short | CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
title_sort | cad-cap: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035135/ https://www.ncbi.nlm.nih.gov/pubmed/32118115 http://dx.doi.org/10.1055/a-1035-9088 |
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