Several studies have shown that inappropriate amounts of fat can considerably raise the risk of many diseases. MSK disorders constitute the world's second leading cause of disability based on years lived with disability (YLDs) [1]. It is estimated that disability resulting from MSK diseases, specifically osteoarthritis (OA), has increased by 45% between 1990 and 2010 and will further grow with an increasingly obese, inactive and ageing population [2]. Thus, accurate and precise measurements of muscle and fat volumes are essential for a better understanding of...

To evaluate the performance of the proposed convolutional neural network (CNN) in this retrospective study, two different test datasets were used and the results of the model on these datasets were compared with some reference segmentation methods that are considered as state-of-the-art in biomedical image segmentation. A CNN model was trained using the training data from these datasets and validated using the test data. Data: Whole-Body MRI: MRI scans (acquired with the Dixon technology) of 14 patients without musculo-skeletal tumor disease and without the presence...

In the first attempt to train the model, the lower limb region was manually segmented with 6 labels, including quadriceps, hamstring group, glutes , bones, subcutaneous fat and intramuscular fat, and trained the model with these labels. The proposed method, achieved good segmentation accuracy in muscle-fat composition, even with a training dataset of only 14 patients, reached an average mean dice coefficient of 0.86 across all classes. The proposed model is modular and non-specific and can therefore be easily adapted to classification tasks in other...

This work presented an accurate machine learning-based method for assessing muscle-fat body composition that fully leverages the combination of all four sequences generated by the Dixon technique. The method becomes fully automatic after the initial segmentation of training samples. Furthermore, this study showed that the problem of limited data can be overcome with this novel model. Although this work is still in progress, it already demonstrates the great potential that a system capable of performing high-quality, automated whole-body MRI segmentations could bring to a variety...

S. Ramedani: Nothing to disclose H. Von Tengg-Kobligk: Nothing to disclose C. Morhard: CEO: CEO of ProKanDo GmbH K. Daneshvar Ghorbani: Nothing to disclose

Vos, T. et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 380, 2163–2196 (2012); Storheim, K. & Zwart, J.-A. Musculoskeletal disorders and the Global Burden of Disease study. Annals of the rheumatic diseases 73, 949–950 (2014); Ma, J. Dixon techniques for water and fat imaging. Journal of magnetic resonance imaging : JMRI 28, 543–558 (2008); Yushkevich, P.A. et al. User-guided 3D active contour segmentation of...