In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a cons...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 6, 2019
Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new...
Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Nov 29, 2019
BACKGROUND: Cell-assisted lipotransfer (CAL) promotes the survival of fat grafts with high vascular density and improves skin quality by increasing collagen content. However, no study has quantified the changes on the skin surface, and rigorous metho...
The aim of this study was to predict carcass tissue composition of goat kids using the decision tree with CHAID algorithm (DT) and artificial neural network (ANN) method in comparison with classical step-wise regression (SWR) analyse. Data were obtai...
PURPOSE: Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdom...
Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on...
One major role of an accurate distribution of abdominal adipose tissue is to predict disease risk. This paper proposes a novel effective three-level convolutional neural network (CNN) approach to automate the selection of abdominal computed tomograph...
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...
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 ...
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...
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