Automated segmentation of abdominal subcutaneous adipose tissue and visceral adipose tissue in obese adolescent in MRI
Publication in policy or professional journal


To develop a reliable and reproducible automatic technique to segment and measure SAT and VAT based on MRI.
Materials and methods:
Chemical-shift water-fat MRI were taken on twelve obese adolescents (mean age: 16.1 ± 0.6, BMI: 31.3 ± 2.3) recruited under the health monitoring program. The segmentation applied a spoke template created using Midpoint Circle algorithm followed by Bresenham's Line algorithm to detect narrow connecting regions between subcutaneous and visceral adipose tissues. Upon satisfaction of given constrains, a cut was performed to separate SAT and VAT. Bone marrow was consisted in pelvis and femur. By using the intensity difference in T2*, a mask was created to extract bone marrow adipose tissue (MAT) from VAT. Validation was performed using a semi-automatic method. Pearson coefficient, Bland-Altman plot and intra-class coefficient (ICC) were applied to measure accuracy and reproducibility.
Pearson coefficient indicated that results from the proposed method achieved high correlation with the semi-automatic method. Bland-Altman plot and ICC showed good agreement between the two methods. Lowest ICC was obtained in VAT segmentation at lower regions of the abdomen while the rests were all above 0.80. ICC (0.98–0.99) also indicated the proposed method performed good reproducibility.
No user interaction was required during execution of the algorithm and the segmented images and volume results were given as output. This technique utilized the feature in the regions connecting subcutaneous and visceral fat and T2* intensity difference in bone marrow to achieve volumetric measurement of various types of adipose tissue in abdominal site.
著者Steve C.N. Hui, Teng Zhang, Lin Shi, Defeng Wang, Chei-Bing Ip, Winnie C.W. Chu
期刊名稱Magnetic Resonance Imaging
頁次97 - 104
關鍵詞MRI, Obese adolescents, Segmentation, Subcutaneous adipose tissue, Visceral adipose tissue

上次更新時間 2021-13-10 於 23:36