title: A method for the automatic segmentation of brown adipose tissue.
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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore.
authors.forename: K N
authors.initials: KN
authors.lastname: Bhanu Prakash
authors.affiliation: Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore. h.srour@uq.edu.au.
authors.forename: Hussein
authors.initials: H
authors.lastname: Srour
authors.affiliation: Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore.
authors.forename: Sendhil S
authors.initials: SS
authors.lastname: Velan
authors.affiliation: Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore.
authors.forename: Kai-Hsiang
authors.initials: KH
authors.lastname: Chuang
mesh_terms: D002001:Adipose Tissue, Brown / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D052436:Adipose Tissue, White / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D000465:Algorithms; D000818:Animals; D005260:Female; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Automated segmentation; Brown adipose tissue; Fat–water imaging; Magnetic resonance imaging; Mouse; White adipose tissue