Radiomics and Deep Learning: Hepatic Applications

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, progno...

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Detalles Bibliográficos
Autores principales: Park, Hyo Jung, Park, Bumwoo, Lee, Seung Soo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2020
Materias:
Gastrointestinal Imaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082656/
https://www.ncbi.nlm.nih.gov/pubmed/32193887
http://dx.doi.org/10.3348/kjr.2019.0752
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author Park, Hyo Jung
Park, Bumwoo
Lee, Seung Soo
author_facet Park, Hyo Jung
Park, Bumwoo
Lee, Seung Soo
author_sort Park, Hyo Jung
collection PubMed
description Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
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spelling pubmed-70826562020-04-01 Radiomics and Deep Learning: Hepatic Applications Park, Hyo Jung Park, Bumwoo Lee, Seung Soo Korean J Radiol Gastrointestinal Imaging Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease. The Korean Society of Radiology 2020-04 2020-03-04 /pmc/articles/PMC7082656/ /pubmed/32193887 http://dx.doi.org/10.3348/kjr.2019.0752 Text en Copyright © 2020 The Korean Society of Radiology http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Gastrointestinal Imaging
Park, Hyo Jung
Park, Bumwoo
Lee, Seung Soo
Radiomics and Deep Learning: Hepatic Applications
title Radiomics and Deep Learning: Hepatic Applications
title_full Radiomics and Deep Learning: Hepatic Applications
title_fullStr Radiomics and Deep Learning: Hepatic Applications
title_full_unstemmed Radiomics and Deep Learning: Hepatic Applications
title_short Radiomics and Deep Learning: Hepatic Applications
title_sort radiomics and deep learning: hepatic applications
topic Gastrointestinal Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082656/
https://www.ncbi.nlm.nih.gov/pubmed/32193887
http://dx.doi.org/10.3348/kjr.2019.0752
work_keys_str_mv AT parkhyojung radiomicsanddeeplearninghepaticapplications
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