Cargando…
Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography
PURPOSE: The aim of this study was to develop and validate a fully-automatic quantification of the hepatorenal index (HRI) calculated by a deep convolutional neural network (DCNN) comparable to the interpretations of radiologists experienced in ultrasound (US) imaging. METHODS: In this retrospective...
Autores principales: | Cha, Dong Ik, Kang, Tae Wook, Min, Ji Hye, Joo, Ijin, Sinn, Dong Hyun, Ha, Sang Yun, Kim, Kyunga, Lee, Gunwoo, Yi, Jonghyon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Ultrasound in Medicine
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8446496/ https://www.ncbi.nlm.nih.gov/pubmed/33966363 http://dx.doi.org/10.14366/usg.20179 |
Ejemplares similares
-
Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography
por: Kim, Kwang Baek, et al.
Publicado: (2015) -
Hepatic steatosis, detected by hepatorenal index in ultrasonography, as a predictor of insulin resistance in obese subjects
por: Isaksen, Victoria T., et al.
Publicado: (2016) -
The role of intraoperative ultrasonography in the diagnosis and management of focal hepatic lesions
por: Joo, Ijin
Publicado: (2015) -
Current status of automated breast ultrasonography
por: Shin, Hee Jung, et al.
Publicado: (2015) -
Identification of Arterial Hyperenhancement in CT and MRI in Patients with Hepatocellular Carcinoma: Value of Unenhanced Images
por: Kim, Mimi, et al.
Publicado: (2019)