The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis.

The aim of the study was to create the efficient tool for semi-automated detection of bone marrow oedema lesions in patients with axial spondyloarthritis (axSpA). MRI examinations of 22 sacroiliac joints of patients with confirmed axSpA-related sacroiliitis (median SPARCC score: 14 points) were included into the study. Design of our algorithm is based on Maksymowych et al. evaluation method and consists of the following steps: manual segmentation of bones (T1W sequence), automated detection of reference signal region, sacroiliac joint central lines and ROIs, a division of ROIs into quadrants, automated detection of inflammatory changes (STIR sequence). As a gold standard, two sets of manual lesion delineations were created. Two approaches to the performance assessment of lesion detection were considered: pixel-wise (detections compared pixel by pixel) and quadrant-wise (quadrant to quadrant). Statistical analysis was performed using Spearman's correlation coefficient. Correlation coefficient obtained for pixel-wise comparison of semi-automated and manual detections was 0.87 (p = 0.001), while for quadrant-wise analysis was 0.83 (p = 0.001). The correlation between two sets of manual detections was 0.91 for pixel-wise comparison (p = 0.001) and 0.88 (p = 0.001) for quadrant-wise approach. Spearman's correlation between two manual assessments was not statistically different from the correlation between semi-automated and manual evaluations, both for pixel- (p = 0.14) and quadrant-wise (p = 0.17) analysis. Average single slice processing time: 0.64 ± 0.30 s. Our method allows for objective detection of bone marrow oedema lesions in patients with axSpA. The quantification of affected pixels and quadrants has comparable reliability to manual assessment.