End to End Colonic Content Assessment: ColonMetry Application

Other authors

Institut Català de la Salut

[Orellana B, Monclús E, Navazo I] Visualization, Virtual Reality and Graphics Interaction Research Group, UPC-BarcelonaTech, Barcelona, Spain. [Bendezú Á] Digestive Department, University Hospital General de Catalunya, Barcelona, Spain. [Malagelada C, Azpiroz F] Servei d’Aparell Digestiu, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Ciberehd, Madrid, Spain

Vall d'Hebron Barcelona Hospital Campus

Publication date

2023-03-23T13:00:59Z

2023-03-23T13:00:59Z

2023-02-28



Abstract

Colon segmentation; Colonic content; Intestinal gas


Segmentación de colon; Contenido colónico; Gas intestinal


Segmentació del còlon; Contingut colònic; Gas intestinal


The analysis of colonic contents is a valuable tool for the gastroenterologist and has multiple applications in clinical routine. When considering magnetic resonance imaging (MRI) modalities, T2 weighted images are capable of segmenting the colonic lumen, whereas fecal and gas contents can only be distinguished in T1 weighted images. In this paper, we present an end-to-end quasi-automatic framework that comprises all the steps needed to accurately segment the colon in T2 and T1 images and to extract colonic content and morphology data to provide the quantification of colonic content and morphology data. As a consequence, physicians have gained new insights into the effects of diets and the mechanisms of abdominal distension.


This work was supported by the Spanish Ministry of Science and Innovation (Proyectos de Generación de Conocimiento), PID2021-122295OB-I00, and Agencia Estatal de Investigación and Fondos FEDER, PID2021-122136OB-C21); Ciberehd is funded by the Instituto de Salud Carlos III.

Document Type

Article


Published version

Language

English

Publisher

MDPI

Related items

Diagnostics;13(5)

https://doi.org/10.3390/diagnostics13050910

info:eu-repo/grantAgreement/ES/PEICTI2021-2023/PID2021-122295OB-I00

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Rights

Attribution 4.0 International

http://creativecommons.org/licenses/by/4.0/

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