Colonic Content Assessment from MRI Imaging Using a Semi-automatic Approach

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dc.contributor.authorCeballos, Victoren_US
dc.contributor.authorMonclús, Evaen_US
dc.contributor.authorVázquez, Pere-Pauen_US
dc.contributor.authorBendezú, Álvaroen_US
dc.contributor.authorMego, Marianelaen_US
dc.contributor.authorMerino, Xavieren_US
dc.contributor.authorAzpiroz, Fernandoen_US
dc.contributor.authorNavazo, Isabelen_US
dc.contributor.editorKozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgiaen_US
dc.date.accessioned2019-09-03T13:49:01Z
dc.date.available2019-09-03T13:49:01Z
dc.date.issued2019
dc.description.abstractThe analysis of the morphology and content of the gut is necessary in order to achieve a better understanding of its metabolic and functional activity. Magnetic resonance imaging (MRI) has become an important imaging technique since it is able to visualize soft tissues in an undisturbed bowel using no ionizing radiation. In the last few years, MRI of gastrointestinal function has advanced substantially. However, few studies have focused on the colon, because the analysis of colonic content is time consuming and cumbersome. This paper presents a semi-automatic segmentation tool for the quantitative assessment of the unprepared colon from MRI images. The techniques developed here have been crucial for a number of clinical experiments.en_US
dc.description.sectionheadersVisual Computing for MRI-based Data
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20191227
dc.identifier.isbn978-3-03868-081-9
dc.identifier.issn2070-5786
dc.identifier.pages17-26
dc.identifier.urihttps://doi.org/10.2312/vcbm.20191227
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20191227
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.8 [Computer Graphics]
dc.subjectApplications
dc.subjectI.4.6 [Image Processing and Computer Vision]
dc.subjectSegmentation
dc.subjectJ.3 [Life and Medical Science]
dc.subjectHealth
dc.titleColonic Content Assessment from MRI Imaging Using a Semi-automatic Approachen_US
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VCBM 19: Eurographics Workshop on Visual Computing for Biology and Medicine