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.
Keywords: Automated segmentation; Brown adipose tissue; Fat–water imaging; Magnetic resonance imaging; Mouse; White adipose tissue.