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2018 | OriginalPaper | Buchkapitel

U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework

verfasst von: Anna Fabijańska, Antoine Vacavant, Marie-Ange Lebre, Ana L. M. Pavan, Diana R. de Pina, Armand Abergel, Pascal Chabrot, Benoît Magnin

Erschienen in: Computer Vision and Graphics

Verlag: Springer International Publishing

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Abstract

This paper presents a novel framework devoted to the detection of HCC (Hepato-Cellular Carcinoma) within hepatic DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences, by a deep learning approach. In clinical routine, radiologists usually consider different phases during contrast injection (before injection; arterial phase; portal phase for instance) for HCC diagnosis. By employing a U-Net architecture, we are able to identify such tumors with a very high accuracy (98.5% of classification rate at best) for a small cohort of patients, which should be confirmed in future works by considering larger groups. We also show in this paper the influence of patch size for this machine learning process, and the positive impact of employing all phases available in DCE-MRI sequences, compared to use only one.
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Metadaten
Titel
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework
verfasst von
Anna Fabijańska
Antoine Vacavant
Marie-Ange Lebre
Ana L. M. Pavan
Diana R. de Pina
Armand Abergel
Pascal Chabrot
Benoît Magnin
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00692-1_28

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