A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review
L Zhang, J Sun, B Jiang, L Wang, Y Zhang… - European journal of hybrid …, 2021 - Springer
Background Artificial intelligence (AI) technology has been increasingly developed and
studied in cardiac imaging. This systematic review summarizes the latest progress of image …
studied in cardiac imaging. This systematic review summarizes the latest progress of image …
Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans
Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the
risk of major adverse cardiac events. As manual quantification of EAT is time-consuming …
risk of major adverse cardiac events. As manual quantification of EAT is time-consuming …
An enhanced deep learning method for the quantification of epicardial adipose tissue
KX Tang, XB Liao, LQ Yuan, SQ He, M Wang… - Scientific Reports, 2024 - nature.com
Epicardial adipose tissue (EAT) significantly contributes to the progression of cardiovascular
diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and …
diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and …
Increased adipose tissue is associated with improved overall survival, independent of skeletal muscle mass in non‐small cell lung cancer
J Tao, J Fang, L Chen, C Liang, B Chen… - Journal of Cachexia …, 2023 - Wiley Online Library
Background The prognostic significance of non‐cancer‐related prognostic factors, such as
body composition, has gained extensive attention in oncological research. Compared with …
body composition, has gained extensive attention in oncological research. Compared with …
Deep learning paradigm and its bias for coronary artery wall segmentation in intravascular ultrasound scans: a closer look
Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate;
therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging …
therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging …
CT-derived epicardial adipose tissue density: Systematic review and meta-analysis
Purpose The aim of our work was to systematically review and meta-analyze epicardial
adipose tissue (EAT) density values reported in literature, assessing potential correlations of …
adipose tissue (EAT) density values reported in literature, assessing potential correlations of …
A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images
Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and
enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most …
enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most …
Segmentation and volume quantification of epicardial adipose tissue in computed tomography images
Y Li, S Song, Y Sun, N Bao, B Yang, L Xu - Medical Physics, 2022 - Wiley Online Library
Background Many cardiovascular diseases are closely related to the composition of
epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable …
epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable …
Evaluation of a deep learning‐enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias
E Sung, S Kyranakis, UA Daimee… - The Journal of …, 2024 - Wiley Online Library
Personalized, image‐based computational heart modelling is a powerful technology that
can be used to improve patient‐specific arrhythmia risk stratification and ventricular …
can be used to improve patient‐specific arrhythmia risk stratification and ventricular …