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Quasi-automatic colon segmentation on T2-MRI images with low user effort

Author
Orellana, B.; Monclús, E.; Brunet, P.; Navazo, I.; Bendezú, Á.; Azpiroz, F.
Type of activity
Presentation of work at congresses
Name of edition
21st International Conference on Medical Image Computing and Computer Assisted Intervention
Date of publication
2018
Presentation's date
2018-09
Book of congress proceedings
Medical Image Computing and Computer Assisted Intervention: MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018: proceedings, part II
First page
638
Last page
647
Publisher
Springer
Repository
http://hdl.handle.net/2117/127790 Open in new window
URL
https://www.springerprofessional.de/en/medical-image-computing-and-computer-assisted-intervention-micca/16118870?tocPage=1 Open in new window
Abstract
About 50% of the patients consulting a gastroenterology clinic report symptoms without detectable cause. Clinical researchers are interested in analyzing the volumetric evolution of colon segments under the effect of different diets and diseases. These studies require noninvasive abdominal MRI scans without using any contrast agent. In this work, we propose a colon segmentation framework designed to support T2-weighted abdominal MRI scans obtained from an unprepared colon. The segmentation proce...
Citation
Orellana, B. [et al.]. Quasi-automatic colon segmentation on T2-MRI images with low user effort. A: International Conference on Medical Image Computing and Computer Assisted Intervention. "Medical Image Computing and Computer Assisted Intervention: MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018: proceedings, part II". Berlín: Springer, 2018, p. 638-647.
Keywords
MRI Segmentation, Medical Diagnosi
Group of research
ViRVIG - Visualisation, Virtual Reality and Graphic Interaction Research Group

Participants

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