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

2020 ◽  
Vol 40 (2) ◽  
pp. 729-739
Author(s):  
Iwona Kucybała ◽  
Zbisław Tabor ◽  
Szymon Ciuk ◽  
Robert Chrzan ◽  
Andrzej Urbanik ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Visceral Adipose Tissue ◽  
Automated Segmentation ◽  
Abdominal Computed Tomography ◽  
Computed Tomography Images ◽  
Visceral Adipose

scholarly journals Data set for renal sinus fat volume and visceral adipose tissue volume on computed tomography

Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1658-1664
Author(s):  
Yoko Murakami ◽  
Yukihiro Nagatani ◽  
Masashi Takahashi ◽  
Mitsuru Ikeda ◽  
Itsuko Miyazawa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Adipose Tissue ◽  
Visceral Adipose Tissue ◽  
Renal Sinus ◽  
Data Set ◽  
Tissue Volume ◽  
Adipose Tissue Volume ◽  
Visceral Adipose ◽  
Renal Sinus Fat ◽  
Fat Volume

scholarly journals Association between visceral adipose tissue volume, measured using computed tomography, and cardio-metabolic risk factors

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Wook Yi ◽  
Keunyoung Kim ◽  
Myungsoo Im ◽  
Soree Ryang ◽  
Eun Heui Kim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Computed Tomography ◽  
Adipose Tissue ◽  
Visceral Adipose Tissue ◽  
Metabolic Risk Factors ◽  
Metabolic Parameters ◽  
Oral Glucose ◽  
Slice Thickness ◽  
Metabolic Risk ◽  
Visceral Adipose

AbstractWe evaluated the associations between metabolic parameters with visceral adipose tissue (VAT) volume in women with prediabetes or type 2 diabetes (T2DM), and we compared the VAT volume with the VAT area. We enrolled women aged > 20 years with prediabetes or T2DM, who underwent oral glucose tolerance test and whose VAT was evaluated using computed tomography (CT) at our institution between 2017 and 2019. All participants underwent unenhanced spiral CT with a 3-mm slice thickness from the level of the diaphragm to the level of the mid-thigh. The two VAT areas were defined as the free drawn area on the levels of the umbilicus and L2 vertebra. The VAT areas were also manually drawn from the level of the diaphragm to the level of the pelvic floor and were used to calculate the VAT volumes by summing all areas with a slice thickness of 3 mm after setting the attenuation values from −45 to −195 Hounsfield Unit. All metabolic characteristics, except blood pressure, were significantly correlated with the VAT volume. The VAT areas measured at the level of the L2 vertebra and umbilicus were correlated with serum triglyceride, high-density lipoprotein cholesterol, and Framingham steatosis index alone. Multivariable regression analyses revealed that the VAT volume was significantly associated with several metabolic parameters. In conclusion, in women with prediabetes and T2DM, the VAT volume acquired from CT-based calculation has more significant correlations with metabolic risk factors compared with the VAT area.

scholarly journals Quantification of visceral adipose tissue by computed tomography and magnetic resonance imaging: reproducibility and accuracy

2019 ◽  
Vol 52 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Lorenzo Carlo Pescatori ◽  
Edoardo Savarino ◽  
Giovanni Mauri ◽  
Enzo Silvestri ◽  
Maurizio Cariati ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Adipose Tissue ◽  
Magnetic Resonance ◽  
Visceral Adipose Tissue ◽  
Resonance Imaging ◽  
Interobserver Reproducibility ◽  
Mri Scans ◽  
Visceral Adipose ◽  
Ct And Mri

Abstract Objective: To evaluate the feasibility of quantifying visceral adipose tissue (VAT) on computed tomography (CT) and magnetic resonance imaging (MRI) scans, using freeware, as well as calculating intraobserver and interobserver reproducibility. Materials and Methods: We quantified VAT in patients who underwent abdominal CT and MRI at our institution between 2010 and 2015, with a maximum of three months between the two examinations. A slice acquired at the level of the umbilicus was selected. Segmentation was performed with the region growing algorithm of the freeware employed. Intraobserver and interobserver reproducibility were evaluated, as was the accuracy of MRI in relation to that of CT. Results: Thirty-one patients (14 males and 17 females; mean age of 57 ± 15 years) underwent CT and MRI (mean interval between the examinations, 28 ± 12 days). The interobserver reproducibility was 82% for CT (bias = 1.52 cm2; p = 0.488), 86% for T1-weighted MRI (bias = −4.36 cm2; p = 0.006), and 88% for T2-weighted MRI (bias = −0.52 cm2; p = 0.735). The intraobserver reproducibility was 90% for CT (bias = 0.14 cm2; p = 0.912), 92% for T1-weighted MRI (bias = −3,4 cm2; p = 0.035), and 90% for T2-weighted MRI (bias = −0.30 cm2; p = 0.887). The reproducibility between T1-weighted MRI and T2-weighted MRI was 87% (bias = −0.11 cm2; p = 0.957). In comparison with the accuracy of CT, that of T1-weighted and T2-weighted MRI was 89% and 91%, respectively. Conclusion: The program employed can be used in order to quantify VAT on CT, T1-weighted MRI, and T2-weighted MRI scans. Overall, the accuracy of MRI (in comparison with that of CT) appears to be high, as do intraobserver and interobserver reproducibility. However, the quantification of VAT seems to be less reproducible in T1-weighted sequences.

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