Abstract：
OBJECTIVE: Bending Asymmetry Index (BAI) has been proposed to characterize the types of scoliotic curve in three-dimensional ultrasound imaging. Scolioscan has demonstrated its validity and reliability in scoliosis assessment with manual assessment-based X-ray imaging. The objective of this study is to investigate the ultrasound-derived BAI method to X-ray imaging of scoliosis, with supplementary information provided for the pre-surgery planning.
METHODS: About 30 pre-surgery scoliosis subjects (9 males and 21 females; Cobb: 50.9 ± 19.7°, range 18°-115°) were investigated retrospectively. Each subject underwent three-posture X-ray scanning supine on a plain mattress on the same day. BAI is an indicator to distinguish structural or non-structural curves through the spine flexibility information obtained from lateral bending spinal profiles. BAI was calculated semi-automatically with manual annotation of vertebral centroids and pelvis level inclination adjustment. BAI classification was validated with the scoliotic curve type and traditional Lenke classification using side-bending Cobb angle measurement (S-Cobb).
RESULTS: 82 curves from 30 pre-surgery scoliosis patients were included. The correlation coefficient was R2 = 0.730 (p < 0.05) between BAI and S-Cobb. In terms of scoliotic curve type classification, all curves were correctly classified; out of 30 subjects, 1 case was confirmed as misclassified when applying to Lenke classification earlier, thus has been adjusted.
CONCLUSIONS: BAI method has demonstrated its inter-modality versatility in X-ray imaging application. The curve type classification and the pre-surgery Lenke classification both indicated promising performances upon the exploratory dataset. A fully-automated of BAI measurement is surely an interesting direction to continue our endeavor. Deep learning on the vertebral-level segmentation should be involved in further study.
METHODS: About 30 pre-surgery scoliosis subjects (9 males and 21 females; Cobb: 50.9 ± 19.7°, range 18°-115°) were investigated retrospectively. Each subject underwent three-posture X-ray scanning supine on a plain mattress on the same day. BAI is an indicator to distinguish structural or non-structural curves through the spine flexibility information obtained from lateral bending spinal profiles. BAI was calculated semi-automatically with manual annotation of vertebral centroids and pelvis level inclination adjustment. BAI classification was validated with the scoliotic curve type and traditional Lenke classification using side-bending Cobb angle measurement (S-Cobb).
RESULTS: 82 curves from 30 pre-surgery scoliosis patients were included. The correlation coefficient was R2 = 0.730 (p < 0.05) between BAI and S-Cobb. In terms of scoliotic curve type classification, all curves were correctly classified; out of 30 subjects, 1 case was confirmed as misclassified when applying to Lenke classification earlier, thus has been adjusted.
CONCLUSIONS: BAI method has demonstrated its inter-modality versatility in X-ray imaging application. The curve type classification and the pre-surgery Lenke classification both indicated promising performances upon the exploratory dataset. A fully-automated of BAI measurement is surely an interesting direction to continue our endeavor. Deep learning on the vertebral-level segmentation should be involved in further study.
摘要：
目的：已提出弯曲不对称指数（BAI）来表征三维超声成像中脊柱侧凸曲线的类型。通过基于手动评估的X射线成像，脊柱侧弯评估已证明了其有效性和可靠性。这项研究的目的是探讨超声衍生的BAI方法对脊柱侧凸的X线成像，为手术前计划提供补充信息。
方法：约30名手术前脊柱侧凸受试者（男性9名，女性21名；Cobb：50.9±19.7°，范围18°-115°)进行回顾性调查。每位受试者在同一天在普通床垫上仰卧进行了三姿X射线扫描。BAI是通过从侧向弯曲脊柱轮廓获得的脊柱灵活性信息来区分结构或非结构曲线的指标。通过手动注释椎体和骨盆水平倾斜度调整，半自动计算BAI。使用侧弯Cobb角测量（S-Cobb），用脊柱侧弯曲线类型和传统Lenke分类验证了BAI分类。
结果：82条来自30例术前脊柱侧凸患者的曲线被纳入。BAI与S-Cobb的相关系数为R2=0.730(p<0.05)。在脊柱侧弯类型分类方面，所有曲线都被正确分类；在30名受试者中，1例应用于Lenke分类时被确认为错误分类，因此进行了调整。
结论：BAI方法已证明其在X射线成像应用中的模态间通用性。曲线类型分类和手术前Lenke分类都表明在探索性数据集上有希望的表现。完全自动化的BAI测量无疑是继续我们努力的有趣方向。椎体水平分割的深度学习应参与进一步的研究。
方法：约30名手术前脊柱侧凸受试者（男性9名，女性21名；Cobb：50.9±19.7°，范围18°-115°)进行回顾性调查。每位受试者在同一天在普通床垫上仰卧进行了三姿X射线扫描。BAI是通过从侧向弯曲脊柱轮廓获得的脊柱灵活性信息来区分结构或非结构曲线的指标。通过手动注释椎体和骨盆水平倾斜度调整，半自动计算BAI。使用侧弯Cobb角测量（S-Cobb），用脊柱侧弯曲线类型和传统Lenke分类验证了BAI分类。
结果：82条来自30例术前脊柱侧凸患者的曲线被纳入。BAI与S-Cobb的相关系数为R2=0.730(p<0.05)。在脊柱侧弯类型分类方面，所有曲线都被正确分类；在30名受试者中，1例应用于Lenke分类时被确认为错误分类，因此进行了调整。
结论：BAI方法已证明其在X射线成像应用中的模态间通用性。曲线类型分类和手术前Lenke分类都表明在探索性数据集上有希望的表现。完全自动化的BAI测量无疑是继续我们努力的有趣方向。椎体水平分割的深度学习应参与进一步的研究。
