Cargando…
Abdominal fat quantification using convolutional networks
OBJECTIVES: To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance—accuracy, reliability, processing effort, and time—in comparison with an interactive referen...
Autores principales: | Schneider, Daniel, Eggebrecht, Tobias, Linder, Anna, Linder, Nicolas, Schaudinn, Alexander, Blüher, Matthias, Denecke, Timm, Busse, Harald |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667157/ https://www.ncbi.nlm.nih.gov/pubmed/37436508 http://dx.doi.org/10.1007/s00330-023-09865-w |
Ejemplares similares
-
Abdominal subcutaneous fat quantification in obese patients from limited field-of-view MRI data
por: Michel, Sophia, et al.
Publicado: (2020) -
Anthropometric estimators of abdominal fat volume in adults with overweight and obesity
por: Michel, Sophia, et al.
Publicado: (2023) -
Half-body MRI volumetry of abdominal adipose tissue in patients with obesity
por: Linder, Nicolas, et al.
Publicado: (2019) -
Dicomflex: A novel framework for efficient deployment of image analysis tools in radiological research
por: Stange, Roland, et al.
Publicado: (2018) -
Age and gender specific estimation of visceral adipose tissue amounts from radiological images in morbidly obese patients
por: Linder, Nicolas, et al.
Publicado: (2016)