Título:
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Quasi-automatic colon segmentation on T2-MRI images with low user effort
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Autor/a:
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Orellana, Bernat; Monclús Lahoya, Eva; Brunet Crossa, Pere; Navazo Álvaro, Isabel; Bendezú García, Álvaro; Azpiroz Vidaur, Fernando
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica |
Abstract:
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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 process is based on an efficient and accurate quasiautomatic approach that drastically reduces the specialist interaction and effort with respect other state-of-the-art solutions, while decreasing
the overall segmentation cost. The algorithm relies on a novel probabilistic tubularity filter, the detection of the colon medial line, probabilistic information extracted from a training set and a final unsupervised clustering.
Experimental results presented show the benefits of our approach for clinical use. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica -Diagnostic imaging -MRI Segmentation -Medical Diagnosi -Imatgeria per al diagnòstic |
Derechos:
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Tipo de documento:
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Artículo - Versión presentada Objeto de conferencia |
Editor:
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Springer
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