Automated abdominal segmentation of CT scans for body composition analysis using deep learning

AD Weston, P Korfiatis, TL Kline, KA Philbrick… - Radiology, 2019 - pubs.rsna.org
Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen
from CT to quantify body composition. Materials and Methods For this retrospective study, a …

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

S Koitka, L Kroll, E Malamutmann, A Oezcelik… - European …, 2021 - Springer
Objectives Body tissue composition is a long-known biomarker with high diagnostic and
prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in …

Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort

SJ Lee, J Liu, J Yao, A Kanarek… - The British journal of …, 2018 - academic.oup.com
Objective: To investigate a fully automated CT-based adiposity tool, applying it to a
longitudinal adult screening cohort. Methods: A validated automated adipose tissue …

An effective CNN method for fully automated segmenting subcutaneous and visceral adipose tissue on CT scans

Z Wang, Y Meng, F Weng, Y Chen, F Lu, X Liu… - Annals of biomedical …, 2020 - Springer
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) …

Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction

D Liu, JW Garrett, MH Lee, R Zea, RM Summers… - Abdominal …, 2023 - Springer
Purpose The purpose of this study is to compare fully automated CT-based measures of
adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which …

Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank

MT MacLean, Q Jehangir, M Vujkovic… - Journal of the …, 2021 - academic.oup.com
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. Materials and …

A fast graph-based algorithm for automated segmentation of subcutaneous and visceral adipose tissue in 3D abdominal computed tomography images

I Kucybała, Z Tabor, S Ciuk, R Chrzan… - Biocybernetics and …, 2020 - Elsevier
The aim of the study was to create an accurate method of automated subcutaneous (SAT)
and visceral (VAT) adipose tissue detection basing on three-dimensional (3D) computed …

An effective automatic segmentation of abdominal adipose tissue using a convolution neural network

C Micomyiza, B Zou, Y Li - … & Metabolic Syndrome: Clinical Research & …, 2022 - Elsevier
Background and aims Computer-aided diagnosis and prognosis rely heavily on fully
automatic segmentation of abdominal fat tissue using Emission Tomography images. The …

Deep learning for abdominal adipose tissue segmentation with few labelled samples

Z Wang, AH Hounye, J Zhang, M Hou, M Qi - International Journal of …, 2022 - Springer
Purpose Fully automated abdominal adipose tissue segmentation from computed
tomography (CT) scans plays an important role in biomedical diagnoses and prognoses …

Using Adipose Measures from Health Care Provider‐Based Imaging Data for Discovery

EDK Cha, Y Veturi, C Agarwal, A Patel… - Journal of …, 2018 - Wiley Online Library
The location and type of adipose tissue is an important factor in metabolic syndrome. A
database of picture archiving and communication system (PACS) derived abdominal …