Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

Journal: International journal of computer assisted radiology and surgery

Published Date: Aug 1, 2019

Abstract

PURPOSE: We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.

Authors

Farid Ouhmich

Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France. farid.ouhmich@ihu-strasbourg.eu.
Vincent Agnus

Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France.
Vincent Noblet

ICube-UMR 7357, Strasbourg, France.
Fabrice Heitz

ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France.
Patrick Pessaux

Institut Hospitalo-Universitaire de Strasbourg (IHU) Digestive and Endocrine Surgery, Nouvel Hôpital Civil, University of Strasbourg, 1, Place de l'Hôpital, 67091, Strasbourg, France, patrick.pessaux@chru-strasbourg.fr.

Keywords

Algorithms Biomarkers Carcinoma, Hepatocellular Deep Learning Humans Image Processing, Computer-Assisted Liver Liver Neoplasms Necrosis Neural Networks, Computer Pattern Recognition, Automated Reproducibility of Results Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (31041697)

Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

Abstract

Authors

Keywords

External Resources

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