AIMC Topic: Subcutaneous Fat

Clear Filters Showing 11 to 20 of 22 articles

Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation.

European journal of radiology Jul 12, 2020
PURPOSE: Aim of this study was to develop and evaluate a software toolkit, which allows for a fully automated body composition analysis in contrast enhanced abdominal computed tomography leveraging the strengths of both, quantitative information from...
Middle Aged Deep Learning Image Processing, Computer-Assisted Muscle, Skeletal Retrospective Studies Humans Tomography, X-Ray Computed Algorithms Body Composition Female Linear Models Reproducibility of Results Radiology Information Systems Male Intra-Abdominal Fat Subcutaneous Fat
View on PubMed DOI

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

European radiology Feb 13, 2020
INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).
Male Middle Aged Disease Progression Deep Learning Lung Neoplasms Reproducibility of Results Aged Adult Survival Rate Body Composition Progression-Free Survival Proportional Hazards Models Body Surface Area Intra-Abdominal Fat Carcinoma, Non-Small-Cell Lung Muscle, Skeletal Positron Emission Tomography Computed Tomography Neoplasm Staging Humans Prognosis Female Subcutaneous Fat Whole Body Imaging
View on PubMed DOI

Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning.

Lifestyle genomics Dec 10, 2019
BACKGROUND: Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provi...
Body Composition Lumbar Vertebrae Cost-Benefit Analysis Pattern Recognition, Automated Lumbosacral Region Subcutaneous Fat Deep Learning Image Processing, Computer-Assisted Body Mass Index Muscle, Skeletal Adipose Tissue Humans Neural Networks, Computer Tomography, X-Ray Computed Reproducibility of Results
View on PubMed DOI

Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning.

Magnetic resonance imaging Apr 18, 2019
Accurate measuring of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) is vital for the research of many diseases. The localization and quantification of SAT and VAT by computed tomography (CT) expose patients to harmful ionizing r...
Aged, 80 and over Adult Humans Male Image Processing, Computer-Assisted Machine Learning Middle Aged Magnetic Resonance Imaging Female Cluster Analysis Reproducibility of Results Intra-Abdominal Fat Aged Subcutaneous Fat
View on PubMed DOI

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

PloS one Sep 20, 2018
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...
Magnetic Resonance Imaging Mice, Inbred C57BL Adipose Tissue Mice, Knockout Body Weight Automation Organ Specificity Abdominal Fat Intra-Abdominal Fat Phenotype Female Animals Male Deep Learning Apolipoproteins E Subcutaneous Fat Diet, Western Mice, Inbred C3H
View on PubMed DOI

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 Mar 21, 2017
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...
Image Interpretation, Computer-Assisted Humans Abdominal Fat Intra-Abdominal Fat Neural Networks, Computer Subcutaneous Fat Tomography, X-Ray Computed Abdomen
View on PubMed DOI

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

Magma (New York, N.Y.) Mar 30, 2016
OBJECTIVES: To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.
Male Subcutaneous Fat Thigh Humans Middle Aged Algorithms Body Composition Machine Learning Imaging, Three-Dimensional Female Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Aged Pattern Recognition, Automated Magnetic Resonance Imaging Aged, 80 and over
View on PubMed DOI

A study of increase in leg volume during complex physical therapy for leg lymphedema using subcutaneous tissue ultrasonography.

Journal of vascular surgery. Venous and lymphatic disorders Jun 15, 2015
OBJECTIVE: The purpose of this study was to discuss the mode of increase in leg volume during complex physical therapy (CPT) for lymphedema using subcutaneous tissue ultrasonography.
Leg Adult Humans Physical Therapy Modalities Leg Injuries Subcutaneous Fat Male Female Leg Ulcer Subcutaneous Tissue Middle Aged Aged Lymphedema Treatment Outcome Aged, 80 and over Thigh
View on PubMed DOI

Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.

Physiological reports Feb 1, 2025
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requi...
Humans Machine Learning Female Muscle, Skeletal Young Adult Thigh Adult Body Composition Pilot Projects Resistance Training Magnetic Resonance Imaging Subcutaneous Fat
View on PubMed DOI

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.

Cancer biomarkers : section A of Disease markers Jan 1, 2024
OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.
Breast Neoplasms Deep Learning Subcutaneous Fat Humans Neural Networks, Computer Tomography, X-Ray Computed Female
View on PubMed DOI
Popular Topics
Recent Journals