Automated MRI quantification of pediatric abdominal adipose tissue using convolutional neural networks and novel total intensity maps

• CNNs to quantify ASAT and VAT from MRI data automatically and accurately. • Crude volumes of ASAT and VAT were transformed to novel Total Intensity Maps. • Created Total Intensity Maps were novel matrix inputs into CNNs. • Fat quantifications were comparable to that obtained by the gold standard. • New readily accessible method without the need for high-power computing resources. Childhood obesity is a significant global health concern. Visceral adipose tissue (VAT) is more closely linked to the development of metabolic and cardiovascular diseases than abdominal subcutaneous adipose tissue (ASAT). This study aimed to develop a straightforward and automated method to quantify both ASAT and VAT in children from MRI. Dixon-based 3 T-MRI scans were conducted on Mexican boys aged 7–9 years. Novel Total Intensity Maps were generated from these scans for input into simple 2D Convolutional Neural Networks (CNNs) for automatic prediction/quantification of ASAT and VAT. The results were compared with the commercial semi-automated quantification and validated method provided by AMRA® Researcher which is the gold standard. The CNN-based method produced ASAT and VAT quantifications comparable to those obtained by AMRA® Researcher (ASAT, p = 0.1765; VAT, p = 0.7757), with a high correlation between the two methods for ASAT (R 2 = 0.98, p < 10 −14 ) and VAT (R 2 = 0.95, p < 10 −11 ). The reproducibility coefficients (RPC) were 0.32 L for ASAT and 0.17 L for VAT with our novel methodology. This study presents a simple automated method for quantifying ASAT and VAT in children from MRI data using 2D-CNN analysis of novel inputs, Total Intensity Maps. The new method produced measurements comparable to those obtained by AMRA® Researcher on the same dataset. The proposed automatic and straightforward quantification method, with minimal computational resource requirements, may serve as an accessible tool for researchers and clinicians in the diagnosis, follow-up, and prevention of childhood obesity.