PURPOSE: To develop an automatic segmentation algorithm to classify abdominal adipose tissues into visceral fat (VAT), deep (DSAT), and superficial (SSAT) subcutaneous fat compartments and evaluate its performance against manual segmentation. MATERIALS AND METHODS: Data were acquired from 44 normal (BMI 18.0-22.9 kg/m(2) ) and 38 overweight (BMI 23.0-29.9 kg/m(2) ) subjects at 3T using a two-point Dixon sequence. A fully automatic segmentation algorithm was developed to segment the fat depots. The first part of the segmentation used graph cuts to separate the subcutaneous and visceral adipose tissues and the second step employed a modified level sets approach to classify deep and superficial subcutaneous tissues. The algorithmic results of segmentation were validated against the ground truth generated by manual segmentation. RESULTS: The proposed algorithm showed good performance with Dice similarity indices of VAT/DSAT/SSAT: 0.92/0.82/0.88 against the ground truth. The study of the fat distribution showed that there is a steady increase in the proportion of DSAT and a decrease in the proportion of SSAT with increasing obesity. CONCLUSION: The presented technique provides an accurate approach for the segmentation and quantification of abdominal fat depots.
PURPOSE: To develop an automatic segmentation algorithm to classify abdominal adipose tissues into visceral fat (VAT), deep (DSAT), and superficial (SSAT) subcutaneous fat compartments and evaluate its performance against manual segmentation. MATERIALS AND METHODS: Data were acquired from 44 normal (BMI 18.0-22.9 kg/m(2) ) and 38 overweight (BMI 23.0-29.9 kg/m(2) ) subjects at 3T using a two-point Dixon sequence. A fully automatic segmentation algorithm was developed to segment the fat depots. The first part of the segmentation used graph cuts to separate the subcutaneous and visceral adipose tissues and the second step employed a modified level sets approach to classify deep and superficial subcutaneous tissues. The algorithmic results of segmentation were validated against the ground truth generated by manual segmentation. RESULTS: The proposed algorithm showed good performance with Dice similarity indices of VAT/DSAT/SSAT: 0.92/0.82/0.88 against the ground truth. The study of the fat distribution showed that there is a steady increase in the proportion of DSAT and a decrease in the proportion of SSAT with increasing obesity. CONCLUSION: The presented technique provides an accurate approach for the segmentation and quantification of abdominal fat depots.
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Authors: Linde van Lee; Sarah R Crozier; Izzuddin M Aris; Mya T Tint; Suresh Anand Sadananthan; Navin Michael; Phaik Ling Quah; Sian M Robinson; Hazel M Inskip; Nicholas C Harvey; Mary Barker; Cyrus Cooper; Sendhil S Velan; Yung Seng Lee; Marielle V Fortier; Fabian Yap; Peter D Gluckman; Kok Hian Tan; Lynette P Shek; Yap-Seng Chong; Keith M Godfrey; Mary F F Chong Journal: Int J Epidemiol Date: 2019-04-01 Impact factor: 7.196
Authors: Yi Ying Ong; Suresh Anand Sadananthan; Izzuddin M Aris; Mya Thway Tint; Wen Lun Yuan; Jonathan Y Huang; Yiong Huak Chan; Sharon Ng; See Ling Loy; Sendhil S Velan; Marielle V Fortier; Keith M Godfrey; Lynette Shek; Kok Hian Tan; Peter D Gluckman; Fabian Yap; Jonathan Tze Liang Choo; Lieng Hsi Ling; Karen Tan; Li Chen; Neerja Karnani; Yap-Seng Chong; Johan G Eriksson; Mary E Wlodek; Shiao-Yng Chan; Yung Seng Lee; Navin Michael Journal: Int J Epidemiol Date: 2020-10-01 Impact factor: 7.196