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Classification of Adipose Tissues using Machine Learning
Brandon Campbell1,2, Gregory Simchick1,2, Hang Yin3, and Qun Zhao1,2

1Physics and Astronomy, University of Georgia, Athens, GA, United States, 2Bio-Imaging Research Center, University of Georgia, Athens, GA, United States, 3Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States

Synopsis

Previous classification techniques for determining the quantification of white adipose tissue and brown adipose tissue have relied on using fat fraction and proton relaxation times using fixed peak spectroscopic models. Machine learning algorithms have proven to be highly accurate for image segmentation but their accuracies rely heavily on input datasets. By using the recently proposed Multi-Varying MR Spectroscopy model an increase in dataset specificity can be applied to each voxel by addition of varying fat peak intensity values. Using this new dataset, four machine learning models were compared.

Introduction

The role of white adipose tissue (WAT) and brown adipose tissue (BAT) in many diseases is well known, therefore the need to accurately classify the volume and distribution of WAT and BAT in the body is clinically useful.1 In recent years, Deep Learning algorithms have proven to be highly successful for image segmentation in MRI. There has been previous work showing success in adipose differentiation using T2, T2* and fat fraction calculations as datasets for various machine learning methods.2,3 However, the success of Deep Learning algorithms rely heavily on the quality of the input dataset, and therefore an increase in the dataset specificity for each voxel should provide an increase in classification accuracy. Two datasets, an Echo dataset and Peak dataset, were created based on the complex echo based signal and a multi-varying peak signal decomposition method respectively.4 The accuracies of four machine learning models were compared to determine the quality of these datasets for fat classification.

Data acquisition and Methods

MRI data was acquired from the inter-capsular BAT (iBAT) and inguinal WAT (igWAT) regions of six healthy C57/BL6 mice using a 7T Agilent small animal imaging scanner. A 2D multi-echo gradient-echo sequence was used, consisting of 12 echoes with a spacing of 0.525ms and initial echo at 5.7ms, FOV of 25x25mm and a matrix of 256x256. Labels were applied to the data based on the known locations of BAT, WAT and muscle in the scanned regions for supervised learning. An Echo dataset was created using the complex echo signal from 12 echoes during data acquisition. The real and imaginary components from the data were organized by alternating real and imaginary components for each echo. A Peak dataset consisting of five spectroscopic peaks derived from the previously proposed Multi-Varying-Peak MR Spectroscopy method (MVP-MRS) plus fat fraction and R2* was produced.4 Previous multi-peak methods assume a fixed-peak amplitude for each spectroscopic fat peaks, and therefore the MVP-MRS model allows these spectroscopic peak amplitudes to be included in a feature set for classification.5

Four different supervised machine learning methods were applied to each dataset to compare their accuracies: Support Vector Machine (SVM), Fully-Connected Neural Network (FCNN), One-dimensional Convolutional Neural Network (1D-CNN), and a Two-dimensional Convolutional Neural Network (2D-CNN). Figure 1 shows the architecture of the models. The inputs for each of the methods except the 2D-CNN were flattened arrays of data from each dataset for each voxel. The inputs for the 2D-CNN were extracted from the data via patches of 9x9 voxels. The center voxel from each input corresponded to the label of the patch, therefore allowing segmentation predictions to be made upon this center voxel.

Results and Discussions

To determine the accuracies of each of the models, ten separate runs were performed for each model and the average value and standard deviation for training and validation accuracies were calculated, Figure 2. Confusion matrices and ROC curves were generated for each of the model’s validation results, Figure 3. The lowest testing and validation accuracies when considering both datasets and every model is the SVM using the Peak dataset with values 95.09% for training and 94.93% for validation respectively. This is an expected result as the SVM is a linear classifier with the least number of parameters determining predictions. The model with the best performance is the 2D-CNN with the Echo dataset (99% and 98%), showing a small improvement over the Peak dataset (97% and 96%). The inclusion of spatial information provided by the 2D-CNN is likely the reason this model shows the best performance.

The confusion matrices and ROC curves in Figure 3 provide extra insight in the classification predictions of each model. The major error in class differentiation comes from the difficulty in separating WAT and BAT. The ROC curves show that in each case, at low confidence values, false negatives for BAT present the biggest problem. This can be seen by the red curve in each ROC plot. The area under this curve tends to be slightly lower in each case compared to the other classes.

Conclusion

Each model presents a considerably high accuracy and could be potentially used to confidently predict quantities of WAT and BAT. However, it is interesting that the Echo dataset is consistently more accurate compared to the Peak dataset. This is possibly due to the increased dimensionality per input for each of the voxels in the dataset. To test the full potential and accuracy of these results, histological results will further confirm the machine learning results.

Acknowledgements

No acknowledgement found.

References

1. Cypess, A. M. et al. Identification and Importance of Brown Adipose Tissue in Adult Humans. N. Engl. J. Med. 360, 1509–1517 (2009).

2. Bhanu Prakash, K. N. et al. Segmentation and characterization of interscapular brown adipose tissue in rats by multi-parametric magnetic resonance imaging. Magn. Reson. Mater. Physics, Biol. Med. 29, 277–286 (2016).

3. Bhanu Prakash, K. N., Srour, H., Velan, S. S. & Chuang, K.-H. A method for the automatic segmentation of brown adipose tissue. Magn. Reson. Mater. Physics, Biol. Med. 29, 287–299 (2016).

4. Simchick, G., J. Wu, G. Shi, H. Yin, and Q. Z. Characterization of Brown Adipose Tissue using Multi-Varying-Peak MR Spectroscopy (MVP-MRS). in Proceedings of the Annual Conference of International Society of Magnetic Resonance in Medicine 3280 (2016).

5. Zancanaro, C. et al. Magnetic resonance spectroscopy investigations of brown adipose tissue and isolated brown adipocytes. J. Lipid Res. 35, 2191–2199 (1994).

Figures

Figure 1: A.) FCNN architecture comprised of 3-layers. The first-layer contains 60 nodes, the second-layer contains 30 nodes, and the final output is 3 nodes corresponding to BAT, WAT and muscle.B.) Five-layer 1D-CNN architecture with alternating convolution and max pooling layers leading to a final fully connected output. C.) Five-layer 2D-CNN architecture with alternating convolution and max pooling layers leading to a final fully connected output. The difference between 1D- and 2D-CNNs are the dimensions of the inputs. The 2D-CNN inputs are 3x3 voxel patches totaling the same number of inputs as the 1D-CNN.

Figure 2: Model accuracies and standard deviations for each of the machine learning models and each dataset. Training accuracies describe the accuracies of the set of data used during the training process and upon which SGD is used to adjust model weights.

Figure 3: Confusion matrices and ROC curves for each of the models and datasets. The confusion matrices show how well each model makes predictions on each class. The rows of the matrices are predicted labels from the machine learning algorithm, while columns are the true labels of the data. ROC curves illustrate how well a model makes predictions on each class at different confidence thresholds. The blue curve represents WAT, the red curve is BAT and the green curve is muscle. The area under each curve is a measure of how accurate the model is for a specific class.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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