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A method for the automatic segmentation of brown adipose tissue

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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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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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Authors and Affiliations

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

    K. N. Bhanu Prakash, Hussein Srour, Sendhil S. Velan & Kai-Hsiang Chuang

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  1. K. N. Bhanu Prakash
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  2. Hussein Srour
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  3. Sendhil S. Velan
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  4. Kai-Hsiang Chuang
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Correspondence to Hussein Srour.

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The authors declare that they have no conflict of interest.

Ethical standard

All animal experiments were approved by and complied with regulations set forth by the local Institutional Animal Care and Use Committee (A*STAR, Singapore).

Additional information

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

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