Open AccessDiagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging
Author(s) -
Róbert Stollmayer,
Bettina Katalin Budai,
Ambrus Tóth,
Ildikó Kalina,
Erika Hartmann,
Péter Szoldán,
Viktor Bérczi,
Pál Maurovich-Horvat,
Pál Novák Kaposi
Publication year - 2021
Publication title -
world journal of gastroenterology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.427
H-Index - 155
eISSN - 2219-2840
pISSN - 1007-9327
DOI - 10.3748/wjg.v27.i35.5978
Subject(s) - magnetic resonance imaging , contrast (vision) , hepatocyte , nuclear magnetic resonance , medicine , pathology , radiology , chemistry , artificial intelligence , computer science , physics , biochemistry , in vitro
The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking. Due to higher information content, three-dimensional input should presumably result in higher classification precision. Also, the differentiation between focal liver lesions (FLLs) can only be plausible with simultaneous analysis of multi-sequence MRI images.