Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, Saudi Arabia.
Department of Radiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
PLoS One. 2024 Mar 21;19(3):e0300685. doi: 10.1371/journal.pone.0300685. eCollection 2024.
Scoliosis is a medical condition in which a person's spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers' variability.
脊柱侧凸是一种医学病症，其特征为脊柱的异常弯曲，而 Cobb 角则是评估脊柱弯曲严重程度的一种测量方法。目前，现有的自动 Cobb 角测量技术需要大量的数据集，耗时且需要大量的工作。因此，开发一种具有良好准确性的 Cobb 角非监督测量方法非常重要。在这项工作中，提出了一种非监督的局部质心（LCM）技术来分割脊柱区域，并进一步提出了一种新的 Cobb 角测量方法以进行准确的测量。在所提出的方法的验证中，使用了来自沙特阿拉伯人群的 2D X 射线图像。将分割结果与基于敏感率、特异性和骰子分数的 GMM 基隐马尔可夫随机场（GMM-HMRF）分割方法进行了比较。根据发现，可以观察到，我们提出的分割方法的总体准确性为 97.3％，而 GMM-HMRF 的准确性为 89.19％。此外，与 GMM-HMRF 相比，我们的方法具有更高的骰子分数 0.54。为了进一步评估该方法在 Cobb 角测量中的有效性，将结果与沙特阿拉伯多专科医院的高级脊柱侧弯外科医生进行了比较。结果表明，脊柱侧凸脊柱的分割几乎是完美的，并且专家和算法通过手动检查获得的 Cobb 角测量值几乎相同，仅相差±3 度。我们提出的方法可以通过减少观察者的变异性，为脊柱侧凸患者的精确脊柱分割和 Cobb 角测量铺平道路。
