A new method for quantitative assessment of hand muscle volume and fat in magnetic resonance images

Abstract

Abstract Background Rheumatoid arthritis (RA) is characterized by systemic inflammation and bone and muscle loss. Recent research showed that obesity facilitates inflammation, but it is unknown if obesity also increases the risk or severity of RA. Further research requires an accurate quantification of muscle volume and fat content. Methods The aim was to develop a reproducible (semi) automated method for hand muscle segmentation and quantification of hand muscle fat content and to reduce the time consuming efforts of manual segmentation. T1 weighted scans were used for muscle segmentation based on a random forest classifier. Optimal segmentation parameters were determined by cross validation with 30 manually segmented hand datasets (gold standard). An operator reviewed the automatically created segmentation and applied corrections if necessary. For fat quantification, the segmentation masks were automatically transferred to MRI Dixon sequences by rigid registration. In total 76 datasets from RA patients were analyzed. Accuracy was validated against the manual gold standard segmentations. Results Average analysis time per dataset was 10 min, more than 10 times faster compared to manual outlining. All 76 datasets could be analyzed and were accurate as judged by a clinical expert. 69 datasets needed minor manual segmentation corrections. Segmentation accuracy compared to the gold standard (Dice ratio 0.98 ± 0.04, average surface distance 0.04 ± 0.10 mm) and reanalysis precision were excellent. Intra- and inter-operator precision errors were below 0.3% (muscle) and 0.7% (fat). Average Hausdorff distances were higher (1.09 mm), but high values originated from a shift of the analysis VOI by one voxel in scan direction. Conclusions We presented a novel semi-automated method for quantitative assessment of hand muscles with excellent accuracy and operator precision, which highly reduced a traditional manual segmentation effort. This method may greatly facilitate further MRI image based muscle research of the hands.