Steve C N Hui1, Teng Zhang1, Lin Shi2, Defeng Wang1, Chei-Bing Ip1, Winnie C W Chu3. 1. Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong. 2. Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong; Chow Yuk Ho Technology Centre for Innovative Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong. 3. Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong. Electronic address: winniechu@cuhk.edu.hk.
Abstract
PURPOSE: 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. RESULTS: 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. CONCLUSION: 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.
PURPOSE: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Steve C N Hui; Simon K H Wong; Qiyong Ai; David K W Yeung; Enders K W Ng; Winnie C W Chu Journal: Eur Radiol Date: 2018-07-30 Impact factor: 5.315
Authors: Sevgi Gokce Kafali; Shu-Fu Shih; Xinzhou Li; Shilpy Chowdhury; Spencer Loong; Samuel Barnes; Zhaoping Li; Holden H Wu Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2021-11
Authors: Chileka Chiyanika; Dorothy F Y Chan; Steve C N Hui; Hung-Kwan So; Min Deng; David K W Yeung; E Anthony S Nelson; Winnie C W Chu Journal: Pediatr Obes Date: 2020-04-29 Impact factor: 4.000