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Adipose Tissue

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Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.

Skeletal radiology
OBJECTIVE: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segme...

Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning.

Molecules (Basel, Switzerland)
This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations ...

Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury.

Scientific reports
Muscle fat infiltration (MFI) of the deep cervical spine extensors has been observed in cervical spine conditions using time-consuming and rater-dependent manual techniques. Deep learning convolutional neural network (CNN) models have demonstrated st...

Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network.

Journal of medical systems
Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat...

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

Magnetic resonance in medicine
PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN).

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Medical physics
PURPOSE: Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is ch...

Water-fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Water-fat separation is a postprocessing technique most commonly applied to multiple-gradient-echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Num...

Deep learning-based quantification of abdominal fat on magnetic resonance images.

PloS one
Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic resonance imaging (MRI) is an accurate method for determination of body fat volume and distribution. How...