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Abstract #0989

An Automatic Machine learning Approach for multi-parametric MR based Brown adipose tissue characterization and Segmentation in mice and rats

Bhanu Prakash KN1, Hussein Srour 1,2, Sanjay Kumar Verma1, Jadegoud Yaligar1, Venkatesh Gopalan1, Swee Shean Lee1, Kai Hsiang Chuang 1,2, and Sendhil Velan S1,3

1Laboratory of Metabolic Imaging, Singapore Bioimaging Consortium, Singapore, Singapore, 2Queensland Brain Institute, Brisbane, Australia, 3MRS & Metabolic Imaging Group, Singapore Institute for Clinical Sciences, Singapore, Singapore

We have utilized multiparametric MR images (fat-fraction (FF), T2 and T2*) of adipose tissues and evaluated different segmentation algorithms like multidimensional thresholding, region growing, clustering, and machine learning approach for its suitability and efficacy to separate WAT from BAT depots. A machine learning algorithm i.e. Neural Network based segmentation provided increased specificity compared to other algorithms. This methodology can be easily extended for multi-parametric human images and longitudinal studies.

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