AIMC Topic: Intra-Abdominal Fat
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Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.
Magma (New York, N.Y.)
Feb 1, 2024
OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with o...
Impact of visceral fat area on short-term outcomes in robotic surgery for mid and low rectal cancer.
Journal of robotic surgery
Jan 30, 2024
Rectal cancer is one of the most prevalent cancers that arise in the digestive tract. The purpose of this retrospective study was to investigate the impact of visceral fat area (VFA) on postoperative outcomes in mid and low rectal cancer patients und...
Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI.
Medical science monitor : international medical journal of experimental and clinical research
Nov 3, 2023
BACKGROUND Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study aimed to examine how visceral fat area (VFA) and body mass index (BMI) correlate with intraoperativ...
Automated Deep Learning-Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study.
AJR. American journal of roentgenology
Aug 16, 2023
The prevalence of childhood obesity has increased significantly worldwide, highlighting a need for accurate noninvasive quantification of body fat distribution in children. The purpose of this study was to develop and test an automated deep learnin...
Gender-specific data-driven adiposity subtypes using deep-learning-based abdominal CT segmentation.
Obesity (Silver Spring, Md.)
May 8, 2023
OBJECTIVE: The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks.
An effective automatic segmentation of abdominal adipose tissue using a convolution neural network.
Diabetes & metabolic syndrome
Aug 10, 2022
BACKGROUND AND AIMS: Computer-aided diagnosis and prognosis rely heavily on fully automatic segmentation of abdominal fat tissue using Emission Tomography images. The identification of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VA...
CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.
Magma (New York, N.Y.)
Aug 2, 2021
BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of ob...
Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.
Clinical nutrition (Edinburgh, Scotland)
Jul 15, 2021
BACKGROUND & AIMS: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation...
Fluorodeoxyglucose F18
Intra-Abdominal Fat
Subcutaneous Fat
Whole Body Imaging
Muscle, Skeletal
Positron Emission Tomography Computed Tomography
Body Composition
Radiopharmaceuticals
Nutrition Assessment
Abdomen
Sarcopenia
Female
Aged
Humans
Male
Middle Aged
Neural Networks, Computer
Retrospective Studies
Republic of Korea
Prognostic implications of body composition change during primary treatment in patients with ovarian cancer: A retrospective study using an artificial intelligence-based volumetric technique.
Gynecologic oncology
May 14, 2021
OBJECTIVE: To investigate the impact of changes in body composition during primary treatment on survival outcomes in patients with epithelial ovarian cancer (EOC).
Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images.
Academic radiology
Aug 6, 2020
RATIONALE AND OBJECTIVES: Develop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images.
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