Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men

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.

[1]  Örjan Smedby,et al.  Quantitative abdominal fat estimation using MRI , 2008, 2008 19th International Conference on Pattern Recognition.

[2]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  F. Pilleul,et al.  Visceral fat accumulation during lipid overfeeding is related to subcutaneous adipose tissue characteristics in healthy men. , 2013, Journal of Clinical Endocrinology and Metabolism.

[4]  P-W Lin,et al.  Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging , 2006, International Journal of Obesity.

[5]  Qi Peng,et al.  Novel segmentation method for abdominal fat quantification by MRI , 2011, Journal of magnetic resonance imaging : JMRI.

[6]  André Tchernof,et al.  Pathophysiology of human visceral obesity: an update. , 2013, Physiological reviews.

[7]  Rasmus Larsen,et al.  Automatic Segmentation of Abdominal Adipose Tissue in MRI , 2011, SCIA.

[8]  Annamalai Sarayu Parimal.  Segmentation of magnetic resonance images of brain and abdomen , 2010 .

[9]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[10]  Thomas Kahn,et al.  Software for automated MRI‐based quantification of abdominal fat and preliminary evaluation in morbidly obese patients , 2013, Journal of magnetic resonance imaging : JMRI.

[11]  Udo Hoffmann,et al.  Abdominal Visceral and Subcutaneous Adipose Tissue Compartments: Association With Metabolic Risk Factors in the Framingham Heart Study , 2007, Circulation.

[12]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Joel Kullberg,et al.  Adipose tissue distribution in children: Automated quantification using water and fat MRI , 2010, Journal of magnetic resonance imaging : JMRI.

[14]  J. Kullberg,et al.  Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI , 2007, International Journal of Obesity.

[15]  J. Kuk,et al.  Measurement site of visceral adipose tissue and prediction of metabolic syndrome in youth , 2011, Pediatric diabetes.

[16]  Jonathan Krakoff,et al.  Distribution of Subcutaneous Fat Predicts Insulin Action in Obesity in Sex‐specific Manner , 2008, Obesity.

[17]  Jimmy D Bell,et al.  Influence of undersampling on magnetic resonance imaging measurements of intra-abdominal adipose tissue , 2003, International Journal of Obesity.

[18]  W. El-Sadr,et al.  Visceral and Subcutaneous Adiposity Measurements in Adults: Influence of Measurement Site , 2007, Obesity.

[19]  Yoshihiko Takahashi,et al.  Associations of Visceral and Subcutaneous Fat Areas With the Prevalence of Metabolic Risk Factor Clustering in 6,292 Japanese Individuals , 2010, Diabetes Care.

[20]  R. Ross,et al.  Ethnic influences on the relations between abdominal subcutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: the International Study of Prediction of Intra-Abdominal Adiposity and Its Relationship With Cardiometabolic Risk/Intra-Abdominal Adiposity. , 2012, The American journal of clinical nutrition.

[21]  F. Karpe,et al.  Structural and Functional Properties of Deep Abdominal Subcutaneous Adipose Tissue Explain Its Association With Insulin Resistance and Cardiovascular Risk in Men , 2014, Diabetes Care.

[22]  B. Goodpaster,et al.  Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. , 2000, American journal of physiology. Endocrinology and metabolism.

[23]  Stampfer,et al.  Abdominal Superficial Subcutaneous Fat: A putative distinct protective fat subdepot in type 2 diabetes , 2012 .

[24]  F. Schick,et al.  Standardized assessment of whole body adipose tissue topography by MRI , 2005, Journal of magnetic resonance imaging : JMRI.

[25]  K. Fox,et al.  Magnetic resonance imaging of abdominal adiposity in a large cohort of British children , 2008, International Journal of Obesity.

[26]  M. Ibrahim.  Subcutaneous and visceral adipose tissue: structural and functional differences , 2010, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[27]  Suresh Anand Sadananthan.,et al.  Retrospective techniques for segmentation of structural and functional MR brain images , 2010 .

[28]  Ronald M. Summers,et al.  Automated measurement and segmentation of abdominal adipose tissue in MRI , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[29]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[30]  N. Lundbom,et al.  Deep subcutaneous adipose tissue is more saturated than superficial subcutaneous adipose tissue , 2013, International Journal of Obesity.

[31]  Krishna S Nayak,et al.  Automatic intra‐subject registration‐based segmentation of abdominal fat from water–fat MRI , 2013, Journal of magnetic resonance imaging : JMRI.

[32]  W. T. Dixon.  Simple proton spectroscopic imaging. , 1984, Radiology.

[33]  J. Scoazec,et al.  Subcutaneous adipose tissue remodeling during the initial phase of weight gain induced by overfeeding in humans. , 2012, The Journal of clinical endocrinology and metabolism.

[34]  Regional differences in abdominal fat loss , 2007, International Journal of Obesity.

[35]  Michael W. L. Chee,et al.  Skull stripping using graph cuts , 2010, NeuroImage.

[36]  W E Reddick,et al.  Fast adipose tissue (FAT) assessment by MRI. , 2000, Magnetic resonance imaging.

[37]  Luigi Landini,et al.  An accurate and robust method for unsupervised assessment of abdominal fat by MRI , 2004, Journal of magnetic resonance imaging : JMRI.

[38]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Jingfei Ma.  Dixon techniques for water and fat imaging , 2008, Journal of magnetic resonance imaging : JMRI.

[40]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.