Textural analysis differentiates brown and white …...Textural analysis differentiates brown and...
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Textural analysis differentiates brown and white adipose tissue on MRI in vivo PURPOSE ² Therapeutic manipulation of brown adipose tissue (BAT) is a promising means of facilitating weight loss in obesity. Assessment of such an approach will require accurate quantification of BAT. ² Histocytological differences between brown and white adipose tissue (WAT), including differences in fat composition, mitochondrial content and vascularity, may manifest as differences in texture on MRI, which may be imperceptible to the naked eye. ² Textural analysis extracts objective data through quantitative analysis of differences in spatial distribution of signal intensity variations between tissues. We hypothesise that BAT and WAT can be differentiated on the basis of texture. Figure 1: Histocytological images [1] showing showing smaller cell size, smaller vacuoles, and higher mitochondrial content in BAT (*) than WAT () METHODS Sixteen patients with BAT activity on 18 F-FDG PET/CT scans underwent Dixon (IDEAL) MRI scanning of the neck and thorax. Images were registered using Mirada XD (Mirada Medical Ltd, Oxford, UK). Regions of interest (ROIs) were defined around putative BAT deposits on PET/ CT by semi-automatically defining iso-contours around putative BAT depots, with a threshold SUV of 2.5 g/ml (figure 2), and transposed onto MRI fat fraction maps. WAT ROIs were defined manually within subcutaneous fat. Figure 2: Semi-automatic segmentation of BAT on PET/CT using an SUV threshold of 2.5 g/ml BAT was confirmed immunohistochemically in a single case [1]. 300+ texture parameters were calculated within each ROI using the open source textural analysis software qMaZda (University of Lodz, Poland). As no single machine learning algorithm is inherently superior [2] multiple machine learning algorithms were evaluated using the open source machine learning software Weka 3.8.1 (University of Waikato, New Zealand). Feature dimensionality was reduced (using the CfsSubsetEval filter) to extract the most important features, and the predictive accuracy of each model in classifying tissue type assessed using 10-fold cross validation. RESULTS The dataset comprised 1,021 BAT ROIs and 587 WAT ROIs across 287 image slices. Ten machine learning algorithms were trained (figure 3). ZeroR (the simplest classification method that merely identifies the most prevalent category) attained 63.5% accuracy. Logistic regression performed best, correctly classifying 1561/1608 ROIs (97.1%) according to their tissue type (97.8% of BAT ROIs, and 95.7% of WAT). Figure 3: Estimates of the accuracy of the machine learning algorithms using tenfold cross-validation (AUC = area under the ROC curve) CONCLUSION Our results support the hypothesis that BAT and WAT can be differentiated in vivo on the basis of textural features extracted from Dixon MRI. Validation of this algorithm on an independent test dataset is required. REFERENCES 1. Reddy N , Jones TA et al (2014). Identification of Brown Adipose Tissue Using MR Imaging in a Human Adult With Histological and Immunohistochemical Confirmation. J Clin Endocrin Metab, 99(1): E117-121. 2. Wolpert DH & Macready WG (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1): 67-82. * Algorithm Algorithm type % accuracy AUC ZeroR Rule 63.5% 0.50 OneR Rule 87.5% 0.86 Naïve Bayes Bayesian 87.7% 0.96 Logistic Regression Function 97.1% 0.99 Simple Logistic Function 95.6% 0.99 Multilayer perceptron Function 96.2% 0.99 Ibk Lazy 94.2% 0.95 AdaBoostM1 Meta 93.0% 0.98 Bagging Meta 96.2% 0.99 J48 Tree 95.1% 0.95 Terence A Jones 1,2 , Abhir Bhalerao 2 , Sarah C Wayte 1 , Thomas Barber 2 , Charles E Hutchinson 1,2 University Hospitals Coventry & Warwickshire NHS Trust, Coventry, UK 1 The University of Warwick, Coventry, UK 2
Transcript of Textural analysis differentiates brown and white …...Textural analysis differentiates brown and...
Textural analysis differentiates brown and white adipose tissue on MRI in vivo
PURPOSE
² Therapeutic manipulation of brown adipose tissue (BAT) is a promising means of facilitating weight loss in obesity. Assessment of such an approach will require accurate quantification of BAT.
² Histocytological differences between brown and white adipose tissue (WAT), including differences in fat composition, mitochondrial content and vascularity, may manifest as differences in texture on MRI, which may be imperceptible to the naked eye.
² Textural analysis extracts objective data through quantitative analysis of differences in spatial distribution of signal intensity variations between tissues. We hypothesise that BAT and WAT can be differentiated on the basis of texture.
Figure 1: Histocytological images [1] showing showing smaller cell size, smaller vacuoles, and higher mitochondrial content in BAT (*) than WAT (�)
METHODS
Sixteen patients with BAT activity on 18F-FDG PET/CT scans underwent Dixon (IDEAL) MRI scanning of the neck and thorax. Images were registered using Mirada XD (Mirada Medical Ltd, Oxford, UK).
Regions of interest (ROIs) were defined around putative BAT deposits on PET/CT by semi-automatically defining iso-contours around putative BAT depots, with a threshold SUV of 2.5 g/ml (figure 2), and transposed onto MRI fat fraction maps. WAT ROIs were defined manually within subcutaneous fat.
Figure 2: Semi-automatic segmentation of BAT on PET/CT using an SUV threshold of 2.5 g/ml
BAT was confirmed immunohistochemically in a single case [1].
300+ texture parameters were calculated within each ROI using the open source textural analysis software qMaZda (University of Lodz, Poland).
As no single machine learning algorithm is inherently superior [2] multiple machine learning algorithms were evaluated using the open source machine learning software Weka 3.8.1 (University of Waikato, New Zealand).
Feature dimensionality was reduced (using the CfsSubsetEval filter) to extract the most important features, and the predictive accuracy of each model in classifying tissue type assessed using 10-fold cross validation.
RESULTS
The dataset comprised 1,021 BAT ROIs and 587 WAT ROIs across 287 image slices.
Ten machine learning algorithms were trained (figure 3).
ZeroR (the simplest classification method that merely identifies the most prevalent category) attained 63.5% accuracy.
Logistic regression performed best, correctly classifying 1561/1608 ROIs (97.1%) according to their tissue type (97.8% of BAT ROIs, and 95.7% of WAT).
Figure 3: Estimates of the accuracy of the machine learning algorithms using tenfold cross-validation (AUC = area under the ROC curve)
CONCLUSION
Our results support the hypothesis that BAT and WAT can be differentiated in vivo on the basis of textural features extracted from Dixon MRI.
Validation of this algorithm on an independent test dataset is required.
REFERENCES
1. Reddy N , Jones TA et al (2014). Identification of Brown Adipose Tissue Using MR Imaging in a Human Adult With Histological and Immunohistochemical Confirmation. J Clin Endocrin Metab, 99(1): E117-121.
2. Wolpert DH & Macready WG (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1): 67-82.
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Algorithm Algorithm type % accuracy AUC ZeroR Rule 63.5% 0.50 OneR Rule 87.5% 0.86 Naïve Bayes Bayesian 87.7% 0.96 Logistic Regression Function 97.1% 0.99 Simple Logistic Function 95.6% 0.99 Multilayer perceptron Function 96.2% 0.99 Ibk Lazy 94.2% 0.95 AdaBoostM1 Meta 93.0% 0.98 Bagging Meta 96.2% 0.99 J48 Tree 95.1% 0.95
Terence A Jones 1,2, Abhir Bhalerao 2, Sarah C Wayte 1, Thomas Barber 2, Charles E Hutchinson 1,2
University Hospitals Coventry & Warwickshire NHS Trust, Coventry, UK1
The University of Warwick, Coventry, UK2
