Anatomical landmarks localization for capsule endoscopy studies

Wireless Capsule Endoscopy is a medical procedure that uses a small, wireless camera to capture images of the inside of the digestive tract. The identification of the entrance and exit of the small bowel and of the large intestine is one of the first tasks that need to be accomplished to read a vide...

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Autores: Laiz Treceño, Pablo, Vitrià i Marca, Jordi, Gilabert Roca, Pere, Wenzek, Hagen, Malagelada Grau, Cristina, Watson, Angus J. M., Seguí Mesquida, Santi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/207961
Acceso en línea:https://hdl.handle.net/2445/207961
Access Level:acceso abierto
Palabra clave:Aprenentatge automàtic
Sistemes classificadors (Intel·ligència artificial)
Anatomia humana
Càpsula endoscòpica
Diagnòstic per la imatge
Machine learning
Learning classifier systems
Human anatomy
Capsule endoscopy
Diagnostic imaging
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spelling Anatomical landmarks localization for capsule endoscopy studiesLaiz Treceño, PabloVitrià i Marca, JordiGilabert Roca, PereWenzek, HagenMalagelada Grau, CristinaWatson, Angus J. M.Seguí Mesquida, SantiAprenentatge automàticSistemes classificadors (Intel·ligència artificial)Anatomia humanaCàpsula endoscòpicaDiagnòstic per la imatgeMachine learningLearning classifier systemsHuman anatomyCapsule endoscopyDiagnostic imagingWireless Capsule Endoscopy is a medical procedure that uses a small, wireless camera to capture images of the inside of the digestive tract. The identification of the entrance and exit of the small bowel and of the large intestine is one of the first tasks that need to be accomplished to read a video. This paper addresses the design of a clinical decision support tool to detect these anatomical landmarks. We have developed a system based on deep learning that combines images, timestamps, and motion data to achieve state-of-the-art results. Our method does not only classify the images as being inside or outside the studied organs, but it is also able to identify the entrance and exit frames. The experiments performed with three different datasets (one public and two private) show that our system is able to approximate the landmarks while achieving high accuracy on the classification problem (inside/outside of the organ). When comparing the entrance and exit of the studied organs, the distance between predicted and real landmarks is reduced from 1.5 to 10 times with respect to previous state-of-the-art methods.Elsevier Ltd2024202420232024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion10 p.application/pdfhttps://hdl.handle.net/2445/207961Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.compmedimag.2023.102243Computerized Medical Imaging and Graphics, 2023, vol. 108https://doi.org/10.1016/j.compmedimag.2023.102243cc-by (c) Pablo Laiz Treceño et al., 2023http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2079612026-05-29T05:05:01Z
dc.title.none.fl_str_mv Anatomical landmarks localization for capsule endoscopy studies
title Anatomical landmarks localization for capsule endoscopy studies
spellingShingle Anatomical landmarks localization for capsule endoscopy studies
Laiz Treceño, Pablo
Aprenentatge automàtic
Sistemes classificadors (Intel·ligència artificial)
Anatomia humana
Càpsula endoscòpica
Diagnòstic per la imatge
Machine learning
Learning classifier systems
Human anatomy
Capsule endoscopy
Diagnostic imaging
title_short Anatomical landmarks localization for capsule endoscopy studies
title_full Anatomical landmarks localization for capsule endoscopy studies
title_fullStr Anatomical landmarks localization for capsule endoscopy studies
title_full_unstemmed Anatomical landmarks localization for capsule endoscopy studies
title_sort Anatomical landmarks localization for capsule endoscopy studies
dc.creator.none.fl_str_mv Laiz Treceño, Pablo
Vitrià i Marca, Jordi
Gilabert Roca, Pere
Wenzek, Hagen
Malagelada Grau, Cristina
Watson, Angus J. M.
Seguí Mesquida, Santi
author Laiz Treceño, Pablo
author_facet Laiz Treceño, Pablo
Vitrià i Marca, Jordi
Gilabert Roca, Pere
Wenzek, Hagen
Malagelada Grau, Cristina
Watson, Angus J. M.
Seguí Mesquida, Santi
author_role author
author2 Vitrià i Marca, Jordi
Gilabert Roca, Pere
Wenzek, Hagen
Malagelada Grau, Cristina
Watson, Angus J. M.
Seguí Mesquida, Santi
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Aprenentatge automàtic
Sistemes classificadors (Intel·ligència artificial)
Anatomia humana
Càpsula endoscòpica
Diagnòstic per la imatge
Machine learning
Learning classifier systems
Human anatomy
Capsule endoscopy
Diagnostic imaging
topic Aprenentatge automàtic
Sistemes classificadors (Intel·ligència artificial)
Anatomia humana
Càpsula endoscòpica
Diagnòstic per la imatge
Machine learning
Learning classifier systems
Human anatomy
Capsule endoscopy
Diagnostic imaging
description Wireless Capsule Endoscopy is a medical procedure that uses a small, wireless camera to capture images of the inside of the digestive tract. The identification of the entrance and exit of the small bowel and of the large intestine is one of the first tasks that need to be accomplished to read a video. This paper addresses the design of a clinical decision support tool to detect these anatomical landmarks. We have developed a system based on deep learning that combines images, timestamps, and motion data to achieve state-of-the-art results. Our method does not only classify the images as being inside or outside the studied organs, but it is also able to identify the entrance and exit frames. The experiments performed with three different datasets (one public and two private) show that our system is able to approximate the landmarks while achieving high accuracy on the classification problem (inside/outside of the organ). When comparing the entrance and exit of the studied organs, the distance between predicted and real landmarks is reduced from 1.5 to 10 times with respect to previous state-of-the-art methods.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/207961
url https://hdl.handle.net/2445/207961
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.compmedimag.2023.102243
Computerized Medical Imaging and Graphics, 2023, vol. 108
https://doi.org/10.1016/j.compmedimag.2023.102243
dc.rights.none.fl_str_mv cc-by (c) Pablo Laiz Treceño et al., 2023
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Pablo Laiz Treceño et al., 2023
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406820840243200
score 15,811543

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