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.

中文翻译：

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半自动算法用于检测轴性脊柱关节炎患者的骨髓水肿病变。

这项研究的目的是创建一种有效的工具，用于半自动检测轴突性脊柱关节炎（axSpA）患者的骨髓水肿病变。这项研究包括对确诊为axSpA相关性sa关节炎的患者的22个sa关节进行MRI检查（中位SPARCC评分：14分）。我们的算法设计基于Maksymowych等人。评估方法包括以下步骤：手动分割骨骼（T1W序列），自动检测参考信号区域，sa关节中心线和ROI，将ROI划分为象限，自动检测炎症变化（STIR序列）。作为金标准，创建了两组手动病变描述。考虑了两种方法来评估病变检测的性能：像素级（检测逐像素比较）和象限级（象限到象限）。使用Spearman相关系数进行统计分析。半自动和手动检测的像素方式比较获得的相关系数是0.87（p = 0.001），象限分析的相关系数是0.83（p = 0.001）。两组手动检测之间的相关性（逐像素比较）为0.91（p = 0.001），按象限进行为0.88（p = 0.001）。在像素分析（p = 0.14）和象限分析（p = 0.17）方面，两次手动评估之间的Spearman相关性与半自动和手动评估之间的相关性在统计上没有差异。平均单片处理时间：0.64±0.30 s。我们的方法可以客观检测axSpA患者的骨髓水肿病变。受影响像素和象限的量化具有与手动评估相当的可靠性。