A method for the automatic segmentation of brown adipose tissue

Prakash, K. N. Bhanu, Srour, Hussein, Velan, Sendhil S. and Chuang, Kai-Hsiang (2016) A method for the automatic segmentation of brown adipose tissue. Magnetic Resonance Materials in Physics, Biology and Medicine, 29 2: 287-299. doi:10.1007/s10334-015-0517-0

Author Prakash, K. N. Bhanu
Srour, Hussein
Velan, Sendhil S.
Chuang, Kai-Hsiang
Title A method for the automatic segmentation of brown adipose tissue
Journal name Magnetic Resonance Materials in Physics, Biology and Medicine   Check publisher's open access policy
ISSN 0968-5243
Publication date 2016-04-01
Year available 2016
Sub-type Article (original research)
DOI 10.1007/s10334-015-0517-0
Open Access Status Not Open Access
Volume 29
Issue 2
Start page 287
End page 299
Total pages 13
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Formatted abstract
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.

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.

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.
Keyword Automated segmentation
Brown adipose tissue
Fat–water imaging
Magnetic resonance imaging
White adipose tissue
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
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