AIMC Topic: Subcutaneous Fat

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Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.

Scientific reports Apr 7, 2025
Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessi...
Aged Aged, 80 and over Male Middle Aged Humans Tomography, X-Ray Computed Female Subcutaneous Fat Muscle, Skeletal Body Composition Psoas Muscles Deep Learning Intra-Abdominal Fat Image Processing, Computer-Assisted Sarcopenia
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Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Jan 30, 2025
BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive cap...
Nutrition Assessment Postoperative Complications Neoplasm Recurrence, Local Bilirubin Humans Survival Rate Machine Learning Intra-Abdominal Fat Male Prognosis Middle Aged Serum Albumin Retrospective Studies Cholangiocarcinoma Hepatectomy Tomography, X-Ray Computed Subcutaneous Fat Bile Duct Neoplasms Female Nutritional Status Aged Preoperative Period
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The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.

Scientific reports Nov 22, 2024
Body composition assessment is very useful for evaluating a patient's status in the clinic, but recognizing, labeling, and calculating the body compositions would be burdensome. This study aims to develop a web-based service that could automate calcu...
Deep Learning Image Processing, Computer-Assisted Body Composition Intra-Abdominal Fat Adult Male Middle Aged Subcutaneous Fat Humans Tomography, X-Ray Computed Female Aged
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Automated segmentation of five different body tissues on computed tomography using deep learning.

Medical physics Sep 2, 2022
PURPOSE: To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tiss...
Tomography, X-Ray Computed Deep Learning Adipose Tissue Subcutaneous Fat Humans Neural Networks, Computer
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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...
Intra-Abdominal Fat Subcutaneous Fat Neural Networks, Computer Tomography, X-Ray Computed Abdominal Fat Humans
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Deep learning for abdominal adipose tissue segmentation with few labelled samples.

International journal of computer assisted radiology and surgery Nov 29, 2021
PURPOSE: Fully automated abdominal adipose tissue segmentation from computed tomography (CT) scans plays an important role in biomedical diagnoses and prognoses. However, to identify and segment subcutaneous adipose tissue (SAT) and visceral adipose ...
Humans Neural Networks, Computer Tomography, X-Ray Computed Deep Learning Abdominal Fat Subcutaneous Fat
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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...
Humans Female Reproducibility of Results Aged Middle Aged Subcutaneous Fat Magnetic Resonance Imaging Obesity Abdominal Fat Intra-Abdominal Fat Deep Learning Cohort Studies Diabetes Mellitus, Type 2
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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...
Intra-Abdominal Fat Subcutaneous Fat Whole Body Imaging Positron Emission Tomography Computed Tomography Humans Body Composition Radiopharmaceuticals Fluorodeoxyglucose F18 Abdomen Aged Sarcopenia Male Neural Networks, Computer Muscle, Skeletal Middle Aged Retrospective Studies Nutrition Assessment Republic of Korea Female
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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).
Prognosis Carcinoma, Ovarian Epithelial Retrospective Studies Intra-Abdominal Fat Tomography, X-Ray Computed Ovarian Neoplasms Subcutaneous Fat Humans Neoplasm Staging Female Middle Aged Body Composition Deep Learning Cohort Studies Muscle, Skeletal
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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.
Abdominal Fat Humans Intra-Abdominal Fat Tomography, X-Ray Computed Subcutaneous Fat Deep Learning
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