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Related Experiment Video

Updated: Mar 27, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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A method for the automatic segmentation of brown adipose tissue.

K N Bhanu Prakash1, Hussein Srour2, Sendhil S Velan1

  • 1Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #02-02 Helios, 11 Biopolis Way, Singapore, 138667, Singapore.

Magma (New York, N.Y.)
|January 13, 2016
PubMed
Summary

Neural networks (NNet) accurately segment brown adipose tissue (BAT) using MRI, offering a promising method for quantifying BAT volume in metabolic research. This technique aids in understanding BAT

Keywords:
Automated segmentationBrown adipose tissueFat–water imagingMagnetic resonance imagingMouseWhite adipose tissue

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Area of Science:

  • Adipose tissue biology
  • Medical imaging
  • Metabolic research

Background:

  • Brown adipose tissue (BAT) is crucial for thermogenesis in mammals and infants.
  • Recent findings confirm BAT presence in adult humans, highlighting its potential for weight loss and metabolic disorder treatment.
  • Accurate BAT volume assessment requires reliable differentiation from white adipose tissue (WAT) and muscle.

Purpose of the Study:

  • To evaluate magnetic resonance (MR) imaging for BAT detection.
  • To assess the efficacy of automated MR feature-based segmentation methods for BAT.
  • To identify the optimal method for BAT segmentation and quantification.

Main Methods:

  • Multi-point Dixon and multi-echo T2 spin-echo MR images were acquired from 12 mice.
  • Four segmentation methods were evaluated: multidimensional thresholding (MTh), region-growing (RG), fuzzy c-means (FCM), and neural-network (NNet).
  • Segmentation accuracy was validated against manual definitions of BAT, WAT, and muscle.

Main Results:

  • The neural-network (NNet) method achieved the highest Dice-Statistical-Index and sensitivity at 89.92%.
  • Fuzzy c-means (FCM) followed with 82.86% accuracy.
  • Region-growing (RG) and multidimensional thresholding (MTh) showed lower performance.

Conclusions:

  • Neural networks (NNet) significantly improve BAT specificity from surrounding tissues using T2 MRI.
  • This automated segmentation method facilitates precise quantification and longitudinal measurement of BAT.
  • The findings support NNet's utility in preclinical models and potential application in human subjects.