Abstract：
To propose a deep learning framework \"SpineCurve-net\" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method\'s Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons\' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.
摘要：
提出一种深度学习框架“SpineCurve-net”，用于自动测量术前脊柱侧凸患者计算机断层扫描（CT）图像的3DCobb角。共分析116例脊柱侧凸患者,分为89名患者（平均年龄32.4±24.5岁）和27名患者（平均年龄17.3±5.8岁）的验证组。通过U网和NURBS网实现椎体识别和曲线拟合，并产生脊柱的非均匀有理B样条（NURBS）曲线。3DCobb角以两种方式测量：预测的3DCobb角（PRED-3D-CA），这是从NURBS曲线导出的平滑角度图中的最大值，和2D映射Cobb角(MAP-2D-CA)，这是由沿投影的2D脊柱曲线的切线向量形成的最大角度。该模型有效地分割了脊柱面罩，捕获容易错过的椎体。辐条核过滤区分椎骨区域，集中脊柱曲线。SpineCurve网络方法的Cobb角(PRED-3D-CA和MAP-2D-CA)测量值与外科医生注释的Cobb角(地面实况,GT)基于2D射线照片，揭示高皮尔逊相关系数分别为0.983和0.934。本文提出了一种自动技术，用于计算术前脊柱侧凸患者的3DCobb角，产生的结果与传统的2DCobb角测量高度相关。鉴于其能够准确表示脊柱畸形的三维性质，这种方法显示了在即将到来的病例中帮助医生制定更精确的手术策略的潜力.
