scholarly journals P11.04: Novel automatic coupled ellipse fitting tool for fetal arm adipose tissue quantification in 2D ultrasound images across gestation

2013 ◽  
Vol 42 (s1) ◽  
pp. 152-152
Author(s):  
S. Rueda ◽  
C.L. Knight ◽  
J.A. Noble ◽  
A.T. Papageorghiou
Keyword(s):  
Adipose Tissue ◽  
Ultrasound Images ◽  
Ellipse Fitting ◽  
2D Ultrasound ◽  
Tissue Quantification

scholarly journals P08.16: Systematic thickness analysis of the adipose tissue around the fetal arm across gestation using 2D ultrasound images

2013 ◽  
Vol 42 (s1) ◽  
pp. 145-145
Author(s):  
S. Rueda ◽  
C. L. Knight ◽  
A. T. Papageorghiou ◽  
J. A. Noble
Keyword(s):  
Adipose Tissue ◽  
Ultrasound Images ◽  
2D Ultrasound

scholarly journals P28.09: Novel automatic method for measuring fetal arm adipose tissue in 2D ultrasound images across gestation

2012 ◽  
Vol 40 (S1) ◽  
pp. 281-281 ◽  
Author(s):  
S. Rueda ◽  
C. L. Knight ◽  
A. T. Papageorghiou ◽  
J. A. Noble
Keyword(s):  
Adipose Tissue ◽  
Ultrasound Images ◽  
Automatic Method ◽  
2D Ultrasound

scholarly journals Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis

2021 ◽  
Vol 10 (2) ◽  
pp. 356
Author(s):  
Lennard Kroll ◽  
Kai Nassenstein ◽  
Markus Jochims ◽  
Sven Koitka ◽  
Felix Nensa
Keyword(s):  
Adipose Tissue ◽  
Deep Learning ◽  
Coronary Artery ◽  
Cardiac Ct ◽  
Coronary Calcification ◽  
Risk Scores ◽  
Agatston Score ◽  
Composition Analysis ◽  
Significance Level ◽  
Tissue Quantification

(1) Background: Epi- and Paracardial Adipose Tissue (EAT, PAT) have been spotlighted as important biomarkers in cardiological assessment in recent years. Since biomarker quantification is an increasingly important method for clinical use, we wanted to examine fully automated EAT and PAT quantification for possible use in cardiovascular risk stratification. (2) Methods: 966 patients with intermediate Framingham risk scores for Coronary Artery Disease referred for coronary calcium scans were included in clinical routine retrospectively. The Coronary Artery Calcium Score (CACS) was extracted and tissue quantification was performed by a deep learning network. (3) Results: The Computed Tomography (CT) segmentations predicted by the network indicated no significant correlation between EAT volume and EAT radiodensity when compared to Agatston score (r = 0.18, r = −0.09). CACS 0 category patients showed significantly lower levels of total EAT and PAT volumes and higher EAT and PAT densities than CACS 1–99 category patients (p < 0.01). Notably, this difference did not reach significance regarding EAT attenuation in male patients. Women older than 50 years, thus more likely to be postmenopausal, were shown to be at higher risk of coronary calcification (p < 0.01, OR = 4.59). CACS 1–99 vs. CACS ≥100 category patients remained below significance level (EAT volume: p = 0.087, EAT attenuation: p = 0.98). (4) Conclusions: Our study proves the feasibility of a fully automated adipose tissue analysis in clinical cardiac CT and confirms in a large clinical cohort that volume and attenuation of EAT and PAT are not correlated with CACS. Broadly available deep learning based rapid and reliable tissue quantification should thus be discussed as a method to assess this biomarker as a supplementary risk predictor in cardiac CT.

Cell-Based Two-Region Competition Algorithm with a Map Framework for Boundary Delineation of a Series of 2D Ultrasound Images

2007 ◽  
Vol 33 (10) ◽  
pp. 1640-1650 ◽  
Author(s):  
Jie-Zhi Cheng ◽  
Chung-Ming Chen ◽  
Yi-Hong Chou ◽  
Curtis S.K. Chen ◽  
Chui-Mei Tiu ◽  
...  
Keyword(s):  
Ultrasound Images ◽  
2D Ultrasound

Automatic Segmentation of the Cerebral Falx and Adjacent Gyri in 2D Ultrasound Images

2015 ◽  
pp. 287-292 ◽  
Author(s):  
Jennifer Nitsch ◽  
Jan Klein ◽  
Dorothea Miller ◽  
Ulrich Sure ◽  
Horst K. Hahn
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Images ◽  
2D Ultrasound
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