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Abdominal Fat

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Chemerin Blood Levels are Associated with MRI Measured Volumes of Abdominal Adipose Tissue Compartments and Lifestyle Choices.

Acta clinica Croatica 29590721
Obesity is a low-grade chronic inflammatory state, in which a cytokine chemerin with its immunometabolic modulation has an important role. We aimed to study in a healthy population relationships between serum chemerin levels, lifestyle choices and ma...
Abdominal Fat Chemokines Humans Intercellular Signaling Peptides and Proteins Intra-Abdominal Fat Life Style Magnetic Resonance Imaging Obesity
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A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.

Computer methods and programs in biomedicine 28495009
Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estima...
Abdomen Abdominal Fat Humans Image Interpretation, Computer-Assisted Intra-Abdominal Fat Neural Networks, Computer Subcutaneous Fat Tomography, X-Ray Computed
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Deep learning-based quantification of abdominal fat on magnetic resonance images.

PloS one 30235253
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...
Abdominal Fat Adipose Tissue Animals Apolipoproteins E Automation Body Weight Deep Learning Diet, Western Female Intra-Abdominal Fat Magnetic Resonance Imaging Male Mice, Inbred C3H Mice, Inbred C57BL Mice, Knockout Organ Specificity Phenotype Subcutaneous Fat
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Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images.

Academic radiology 32771313
RATIONALE AND OBJECTIVES: Develop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images.
Abdominal Fat Deep Learning Humans Intra-Abdominal Fat Subcutaneous Fat Tomography, X-Ray Computed
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Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank.

Journal of the American Medical Informatics Association : JAMIA 33576413
OBJECTIVE: The objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank.
Abdominal Fat Biological Specimen Banks Deep Learning Electronic Health Records Humans Tomography, X-Ray Computed
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