Automatic colon segmentation on T1-FS MR images

The volume and distribution of the colonic contents provides valuable insights into the effects of diet on gut microbiotica involving both clinical diagnosis and research. In terms of Magnetic Resonance Imaging modalities, T2-weighted images allow the segmentation of the colon lumen, while fecal and...

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Autores: Orellana Bech, Bernat, Navazo Álvaro, Isabel|||0000-0001-6298-1463, Brunet Crosa, Pere|||0000-0001-8406-1975, Monclús Lahoya, Eva|||0000-0002-9645-0510, Bendezú García, Álvaro, Azpiroz Vidaur, Fernando
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/428306
Acceso en línea:https://hdl.handle.net/2117/428306
https://dx.doi.org/10.1016/j.compmedimag.2025.102528
Access Level:acceso abierto
Palabra clave:Medical image analysis
MRI colon segmentation
Multimodality registration
Gastroenterology
Colon contents
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
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spelling Automatic colon segmentation on T1-FS MR imagesOrellana Bech, BernatNavazo Álvaro, Isabel|||0000-0001-6298-1463Brunet Crosa, Pere|||0000-0001-8406-1975Monclús Lahoya, Eva|||0000-0002-9645-0510Bendezú García, ÁlvaroAzpiroz Vidaur, FernandoMedical image analysisMRI colon segmentationMultimodality registrationGastroenterologyColon contentsÀrees temàtiques de la UPC::Ciències de la salutÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàticaThe volume and distribution of the colonic contents provides valuable insights into the effects of diet on gut microbiotica involving both clinical diagnosis and research. In terms of Magnetic Resonance Imaging modalities, T2-weighted images allow the segmentation of the colon lumen, while fecal and gas contents can be only distinguished on the T1-weighted Fat-Sat modality. However, the manual segmentation of T1-weighted Fat-Sat is challenging, and no automatic segmentation methods are known. This paper proposed a non-supervised algorithm providing an accurate T1-weighted Fat-Sat colon segmentation via the registration of an existing colon segmentation in T2-weighted modality. The algorithm consists of two phases. It starts with a registration process based on a classical deformable registration method, followed by a novel Iterative Colon Registration process that utilizes a mesh deformation approach. This approach is guided by a probabilistic model that provides the likelihood of the colon boundary, followed by a shape preservation process of the colon segmentation on T2-weighted images. The iterative process converges to achieve an optimal fit for colon segmentation in T1-weighted Fat-Sat images. The segmentation algorithm has been tested on multiple datasets (154 scans) and acquisition machines (3) as part of the proof of concept for the proposed methodology. The quantitative evaluation was based on two metrics: the percentage of ground truth labeled feces correctly identified by our proposal (93+-5% ), and the volume variation between the existing colon segmentation in the T2-weighted modality and the colon segmentation computed in T1-weighted Fat-Sat images. Quantitative and medical evaluations demonstrated a degree of accuracy, usability, and stability concerning the acquisition hardware, making the algorithm suitable for clinical application and research.This work was supported in part by the projects PID2021-122295OB-I00 (Ministerio de Ciencia e Innovación, Spain), the project PID2021-122136OB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and ERDF “A way of making Europe”, by the EU Horizon 2020 and the Department of Research and Universities of the Government of Catalonia (2021 SGR 01035). Ciberehd is funded by the Instituto de Salud Carlos III, Spain.Peer Reviewed20252025-07-0120252025-04-23journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/428306https://dx.doi.org/10.1016/j.compmedimag.2025.102528reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122295OB-I00 NEUROFISIOLOGIA Y NEUROFISIOPATOLOGIA DIGESTIVAAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122136OB-C21 ENTORNOS 3D DE ALTA FIDELIDAD PARA REALIDAD VIRTUAL Y COMPUTACION VISUAL: GEOMETRIA, MOVIMIENTO, INTERACCION Y VISUALIZACION PARA SALUD, ARQUITECTURA Y CIUDADESopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4283062026-05-27T15:37:01Z
dc.title.none.fl_str_mv Automatic colon segmentation on T1-FS MR images
title Automatic colon segmentation on T1-FS MR images
spellingShingle Automatic colon segmentation on T1-FS MR images
Orellana Bech, Bernat
Medical image analysis
MRI colon segmentation
Multimodality registration
Gastroenterology
Colon contents
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
title_short Automatic colon segmentation on T1-FS MR images
title_full Automatic colon segmentation on T1-FS MR images
title_fullStr Automatic colon segmentation on T1-FS MR images
title_full_unstemmed Automatic colon segmentation on T1-FS MR images
title_sort Automatic colon segmentation on T1-FS MR images
dc.creator.none.fl_str_mv Orellana Bech, Bernat
Navazo Álvaro, Isabel|||0000-0001-6298-1463
Brunet Crosa, Pere|||0000-0001-8406-1975
Monclús Lahoya, Eva|||0000-0002-9645-0510
Bendezú García, Álvaro
Azpiroz Vidaur, Fernando
author Orellana Bech, Bernat
author_facet Orellana Bech, Bernat
Navazo Álvaro, Isabel|||0000-0001-6298-1463
Brunet Crosa, Pere|||0000-0001-8406-1975
Monclús Lahoya, Eva|||0000-0002-9645-0510
Bendezú García, Álvaro
Azpiroz Vidaur, Fernando
author_role author
author2 Navazo Álvaro, Isabel|||0000-0001-6298-1463
Brunet Crosa, Pere|||0000-0001-8406-1975
Monclús Lahoya, Eva|||0000-0002-9645-0510
Bendezú García, Álvaro
Azpiroz Vidaur, Fernando
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Medical image analysis
MRI colon segmentation
Multimodality registration
Gastroenterology
Colon contents
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
topic Medical image analysis
MRI colon segmentation
Multimodality registration
Gastroenterology
Colon contents
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
description The volume and distribution of the colonic contents provides valuable insights into the effects of diet on gut microbiotica involving both clinical diagnosis and research. In terms of Magnetic Resonance Imaging modalities, T2-weighted images allow the segmentation of the colon lumen, while fecal and gas contents can be only distinguished on the T1-weighted Fat-Sat modality. However, the manual segmentation of T1-weighted Fat-Sat is challenging, and no automatic segmentation methods are known. This paper proposed a non-supervised algorithm providing an accurate T1-weighted Fat-Sat colon segmentation via the registration of an existing colon segmentation in T2-weighted modality. The algorithm consists of two phases. It starts with a registration process based on a classical deformable registration method, followed by a novel Iterative Colon Registration process that utilizes a mesh deformation approach. This approach is guided by a probabilistic model that provides the likelihood of the colon boundary, followed by a shape preservation process of the colon segmentation on T2-weighted images. The iterative process converges to achieve an optimal fit for colon segmentation in T1-weighted Fat-Sat images. The segmentation algorithm has been tested on multiple datasets (154 scans) and acquisition machines (3) as part of the proof of concept for the proposed methodology. The quantitative evaluation was based on two metrics: the percentage of ground truth labeled feces correctly identified by our proposal (93+-5% ), and the volume variation between the existing colon segmentation in the T2-weighted modality and the colon segmentation computed in T1-weighted Fat-Sat images. Quantitative and medical evaluations demonstrated a degree of accuracy, usability, and stability concerning the acquisition hardware, making the algorithm suitable for clinical application and research.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-07-01
2025
2025-04-23
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/428306
https://dx.doi.org/10.1016/j.compmedimag.2025.102528
url https://hdl.handle.net/2117/428306
https://dx.doi.org/10.1016/j.compmedimag.2025.102528
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122295OB-I00 NEUROFISIOLOGIA Y NEUROFISIOPATOLOGIA DIGESTIVA
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122136OB-C21 ENTORNOS 3D DE ALTA FIDELIDAD PARA REALIDAD VIRTUAL Y COMPUTACION VISUAL: GEOMETRIA, MOVIMIENTO, INTERACCION Y VISUALIZACION PARA SALUD, ARQUITECTURA Y CIUDADES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
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