Abstract
Objective
Brown adipose tissue (BAT) plays a key role for thermogenesis in mammals and infants. Recent confirmation of BAT presence in adult humans has aroused great interest for its potential to initiate weight-loss and normalize metabolic disorders in diabetes and obesity. Reliable detection and differentiation of BAT from the surrounding white adipose tissue (WAT) and muscle is critical for assessment/quantification of BAT volume. This study evaluates magnetic resonance (MR) acquisition for BAT and the efficacy of different automated methods for MR features-based BAT segmentation to identify the best suitable method.
Materials and methods
Multi-point Dixon and multi-echo T2 spin-echo images were acquired from 12 mice using an Agilent 9.4T scanner. Four segmentation methods: multidimensional thresholding (MTh); region-growing (RG); fuzzy c-means (FCM) and neural-network (NNet) were evaluated for the interscapular region and validated against manually defined BAT, WAT and muscle.
Results
Statistical analysis of BAT segmentation yielded a median Dice-Statistical-Index, and sensitivity of 89. 92 % for NNet, 82. 86 % for FCM, 72. 74 % for RG, and 72. 70 %, for MTh, respectively.
Conclusion
This study demonstrates that NNet improves the specificity to BAT from surrounding tissue based on 3-point Dixon and T2 MRI. This method facilitates quantification and longitudinal measurement of BAT in preclinical-models and human subjects.
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References
Cinti S (2011) Between brown and white: novel aspects of adipocyte differentiation. Ann Med 43(2):104–115
Nedergaard J, Bengtsson T, Cannon B (2007) Unexpected evidence for active brown adipose tissue in adult humans. Am J Physiol Endocrinol Metab 293:E444–E452
Cannon B, Nedergaard J (2004) Brown adipose tissue: function and physiological significance. Physiol Rev 84:277–359
Lichtenbelt WDM, Vanhommerig JW, Smulders NM, Drossaerts JMAFL, Kemerink GJ et al (2009) Cold-activated brown adipose tissue in healthy men. N Engl J Med 2009(360):1500–1508
Cypess A, Lehman S, Williams G, Tal I, Rodman D et al (2009) Identification and importance of brown adipose tissue in adult humans. N Engl J Med 360(15):1509–1517
Saito M, Okamatsu-Ogura Y, Matsushita M, Watanabe K, Yoneshiro T et al (2009) High incidence of metabolically active brown adipose tissue in healthy adult humans: effects of cold exposure and adiposity. Diabetes 58(7):1526–1531
Van der Lans A, Wierts R, Vosselman M, Schrauwen P, Brans B et al (2014) Cold-activated brown adipose tissue in human adults: methodological issues. Am J Physiol Regul Integr Comp Physiol 307(2):R103–R113
Lee P, Greenfield J, Ho K, Fulham M (2010) A critical appraisal of the prevalence and metabolic significance of brown adipose tissue in adult humans. Am J Physiol Endocrinol Metab 299(4):E601–E606
Ouellet V, Labbé S, Blondin D, Phoenix S, Guérin B et al (2012) Brown adipose tissue oxidative metabolism contributes to energy expenditure during acute cold exposure in humans. J Clin Investig 122(2):545–552
Brix G, Lechel U, Glatting G, Ziegler S, Münzing W et al (2005) Radiation exposure of patients undergoing whole-body dual-modality 18F-FDG PET/CT examinations. J Nucl Med 46(4):608–613
Huang B, Law M, Khong P (2009) Whole-body PET/CT scanning: estimation of radiation dose and cancer risk. Radiology 251(1):166–174
Henkelman R (1992) New imaging technologies: prospects for target definition. Int J Radiat Oncol Biol Phys 22(2):251–257
Hu H, Nayak K (2010) Change in the proton T(1) of fat and water in mixture. Magn Reson Med 63(2):494
Strobel K, van den Hoff J, Pietzsch J (2008) Localized proton magnetic resonance spectroscopy of lipids in adipose tissue at high spatial resolution in mice in vivo. J Lipid Res 49(2):473–480
Zingaretti M, Crosta F, Vitali A, Guerrieri M, Frontini A et al (2009) The presence of UCP1 demonstrates that metabolically active adipose tissue in the neck of adult humans truly represents brown adipose tissue. FASEB J 23(9):3113–3120
Hu H, Börnert P, Hernando D, Kellman P, Ma J et al (2012) ISMRM workshop on fat–water separation: insights, applications and progress in MRI. Magn Reson Med 68(2):378–388
Peng X, Ju S, Fang F, Wang Y, Fang K et al (2013) Comparison of brown and white adipose tissue fat fractions in ob, seipin, and Fsp27 gene knockout mice by chemical shift-selective imaging and 1H-MR spectroscopy. Am J Physiol Endocrinol Metab 304(2):E160–E167
Hu H, Perkins TG, Chia JM, Gilsanz V (2013) Characterization of human brown adipose tissue by chemical-shift water–fat MRI. AJR Am J Roentgenol 200(1):177–183
Reeder S, Sirlin C (2010) Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin N Am 18(3):337–357
Lunati E, Marzola P, Nicolato E, Fedrigo M, Villa M, Sbarbati A (1999) In vivo quantitative lipidic map of brown adipose tissue by chemical shift imaging at 4.7 Tesla. J Lipid Res 40(8):1395–1400
Branca R, Warren W (2011) In vivo brown adipose tissue detection and characterization using water–lipid intermolecular zero quantum coherences. Magn Reson Med 65(2):313–319
Borga M, Virtanen K, Romu T, Leinhard O, Persson A et al (2014) Brown adipose tissue in humans: detection and functional analysis using PET (positron emission tomography), MRI (magnetic resonance imaging), and DECT (dual energy computed tomography). Methods Enzymol 537:141–159
Lee P, Brychta R, Linderman J, Smith S, Chen K, Celi F (2013) Mild cold exposure modulates fibroblast growth factor 21 (FGF21) diurnal rhythm in humans: relationship between FGF21 levels, lipolysis, and cold-induced thermogenesis. J Clin Endocrinol Metab 98(1):E98–E102
Chen Y, Cypess A, Sass C, Brownell A, Jokivarsi K et al (2012) Anatomical and functional assessment of brown adipose tissue by magnetic resonance imaging. Obesity (Silver Spring, Md) 20(7):1519–1526
Positano V, Gastaldelli A, Sironi AM, Santarelli MF, Lombardi M, Landini L (2004) An accurate and robust method for unsupervised assessment of abdominal fat by MRI. J Magn Reson Imaging 20(4):684–689
Kullberg J, Ahlström H, Johansson L, Frimmel H (2007) Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI. Int J Obes (Lond) 31(12):1806–1817
Liou TH, Chan WP, Pan LC, Lin PW, Chou P, Chen CH (2006) Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging. Int J Obes (Lond) 30(5):844–852
Ranefall P, Bidar AW, Hockings PD (2009) Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. J Magn Reson Imaging 30(3):554–560
Tang Y, Sharma P, Nelson MD, Simerly R, Moats RA (2011) Automatic abdominal fat assessment in obese mice using a segmental shape model. J Magn Reson Imaging 34(4):866–873
Rasmussen JM, Entringer S, Nguyen A, van Erp TGM, Guijarro A, Oveisi F et al (2013) Brown adipose tissue quantification in human neonates using water–fat separated MRI. PLoS ONE 8(10):e77907. doi:10.1371/journal.pone.0077907
Sandouk A, Bagci U, Xu Z, Mansoor A, Foster B, Mollura D (2013) Accurate quantification of brown adipose tissue through PET-guided CT image segmentation. J Nucl Med 54(2):318
Hu HH, Wu T-W, Yin L, Kim MS, Chia JM, Perkins TG, Gilsanz V (2014) MRI detection of brown adipose tissue with low fat content in newborns with hypothermia. Magn Reson Imaging 32(2):107–117. doi:10.1016/j.mri.2013.10.003
Hardy PA, Hinks RS, Tkach JA (1995) Seperation of fat and water in fast spin echo MR imaging with the three point Dixon technique. J Magn Reson Imaging 5:181–185
Berglund J, Johansson L, Ahlström H, Kullberg J (2010) Three-point dixon method enables whole-body water and fat imaging of obese subjects. Magn Reson Med 63(6):1659–1668
Hu HH, Wu T-W, Yin L et al (2014) MRI detection of brown adipose tissue with low fat content in newborns with hypothermia. Magn Reson Imaging 32(2):107–117
Tam CS, Lecoultre V, Ravussin E (2012) Brown adipose tissue: mechanisms and potential therapeutic targets. Circulation 125(22):2782–2791. doi:10.1161/CIRCULATIONAHA.111.042929
Symonds ME (2013) Brown adipose tissue growth and development. Scientifica 2013:305763. doi:10.1155/2013/305763
Bartelt A, Heeren J (2014) Adipose tissue browning and metabolic health. Nat Rev Endocrinol 10:24–36
Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128
Kriegel HP, Kroger P, Zimek A (2010) Outlier detection techniques. In: The 2010 SIAM international conference on data mining, tutorial notes 2010
Kriegel H-P, Kröger P, Schubert E, Zimek A (2009a) LoOP: local outlier probabilities. In: Proceedings ACM conference on information and knowledge management (CIKM), Hong Kong, China
Ben-Gal I (2005) Outlier detection. In: Maimon O, Rockach L (eds) Data mining and knowledge discovery handbook: a complete guide for practitioners and researchers. Kluwer Academic Publishers. ISBN: 0-387-24435-2
Bezdek JC, Hall LO, Clarke LP (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20(4):1033–1048
Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37
Du KL (2010) Clustering: a neural network approach. Neural Netw 23:89–107
Stekh Y, Sardieh FME, Lobar M (2009) Neural network based clustering algorithm. In: Proceedings of the 5th international conference, perspective technologies and methods in MEMS design, Zakarpattya, pp 168–169
Pratt WK (2007) Digital image processing: PIKS inside. Wiley, Los Altos
Gonzalez R, Woods R (2002) Digital image processing. Prentice Hall, New Jersey
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J Cybern 3:32–57
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, NY
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203
Egmont-Petersen M, de Ridder D, Handels H (2002) Image processing with neural networks a review. Pattern Recognit 35(10):2279–2301
Zou KH, Warfield SK, Bharatha A, Tempany MC, Kaus MR et al (2004) Statistical validation of image segmentation quality based on a spatial overlap. Acad Radiol 11(2):178–189
Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH et al (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58(10):982–990
Srour H, Chuang KH (2015) Functional imaging of brown fat in mouse. In: ISMRM 2015. Abstract—4675
Acknowledgments
The work was supported by the Intramural Research program of the Singapore Bioimaging Consortium, Biomedical Sciences Institutes, Agency for Science, Technology and Research (A*STAR), Singapore.
Authors contribution
Bhanu Prakash KN, Hussein Srour, Kai-Hsiang Chuang: Protocol/project development; Hussein Srour: Data collection or management; Bhanu Prakash KN, S. Sendhil Velan: Data analysis.
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The authors declare that they have no conflict of interest.
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All animal experiments were approved by and complied with regulations set forth by the local Institutional Animal Care and Use Committee (A*STAR, Singapore).
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K. N. Bhanu Prakash and Hussein Srour are to be considered as joint first authors.
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Bhanu Prakash, K.N., Srour, H., Velan, S.S. et al. A method for the automatic segmentation of brown adipose tissue. Magn Reson Mater Phy 29, 287–299 (2016). https://doi.org/10.1007/s10334-015-0517-0
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DOI: https://doi.org/10.1007/s10334-015-0517-0