Title: Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks Show Chinese title

Title: 通过级联神经网络集合的脊柱中线提取自动估计脊柱弯曲度

Abstract: Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual assessment by experts and the automated predictions. For optimization, we used 609 X-ray scans from the London Health Sciences Center, and for evaluation, we participated in the international challenge "Accurate Automated Spinal Curvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, we obtained an average symmetric mean absolute percentage error of 22.96. Show Chinese abstract

Abstract: 脊柱侧弯是一种由异常脊柱弯曲定义的状况。 对于脊柱侧弯的诊断和治疗计划，可以使用Cobb角来估计脊柱弯曲度。 我们提出了一种从X光扫描中自动估计Cobb角的方法。 首先，使用两个卷积神经网络的级联对脊柱的中心线进行分割。 在对中心线进行平滑处理后，使用中心线的导数自动估计Cobb角。 我们使用手动评估与自动化预测之间的平均绝对误差和平均对称平均绝对百分比误差来评估结果。 为了优化，我们使用了来自伦敦健康科学中心的609张X光扫描图像，在评估方面，我们参加了国际挑战“准确的自动脊柱弯曲度估计，MICCAI 2019”（100张扫描图像）。 在挑战的测试集上，我们获得了22.96的平均对称平均绝对百分比误差。