2026-04-03T22:50:00Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/130182025-10-24T10:24:45Zcom_2072_378070com_2072_378040col_2072_378092

00925njm 22002777a 4500 dc Orellana Bech, Bernat author Navazo, Isabel author Brunet, Pere author Monclús, Eva author Bendezú García, Rogger Alvaro author Azpiroz, Fernando author 2025-04-30T09:43:12Z 2025-04-30T09:43:12Z 2025-07 Colon contents; Colon segmentation; Medical image analysis Contingut del còlon; Segmentació del còlon; Anàlisi d'imatges mèdiques Contenido del colon; Segmentación del colon; Análisis de imágenes médicas 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 ( ), 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 . http://hdl.handle.net/11351/13018 Imatges - Segmentació Còlon - Imatgeria per ressonància magnètica ANATOMY::Fluids and Secretions::Gastrointestinal Contents Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging ANATOMÍA::líquidos y secreciones::contenido digestivo Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética Automatic colon segmentation on T1-FS MR images