In clinical practice, the Cobb angle is the gold standard for idiopathic scoliosis assessment, which can provide an important reference for clinicians to make surgical plan and give medical care to patients. Currently, the Cobb angle is measured manually on both anterior-posterior(AP) view X-rays and lateral(LAT) view X-rays. The clinicians first find four landmarks on each vertebra, and then they extend the line from landmarks and measure the Cobb angle by rules. The whole process is time-consuming and subjective, so that the automated Cobb angle estimation is required for efficient and reliable Cobb angle measurement. The noise in X-rays and the occlusion of vertebras are the main difficulties for automated Cobb angle estimation, and it is challenging to utilize the information between the multi-view X-rays of the same patient. Addressing these problems, in this paper, we propose an effective framework named MPF-net by using deep learning methods for automated Cobb angle estimation. We combine a vertebra detection branch and a landmark prediction branch based on the backbone convolutional neural network, which can provide the bounded area for landmark prediction. Then we propose a proposal correlation module to utilize the information between neighbor vertebras, so that we can find the vertebras hidden by ribcage and arms on LAT X-rays. We also design a feature fusion module to utilize the information in both AP and LAT X-rays for better performance. The experiment results on 2738 pair of X-rays show that our proposed MPF-net achieves precise vertebra detection and landmark prediction performance, and we get impressive 3.52 and 4.05 circular mean absolute errors on AP and LAT X-rays respectively, which is much better than previous methods. Therefore, we can provide clinicians with automated, efficient and reliable Cobb angle measurement.

在临床实践中，Cobb角是评估特发性脊柱侧凸的金标准，可以为临床医生制定手术计划和对患者进行医疗护理提供重要参考。目前，科布角是通过前后 (AP) 视图 X 射线和侧视图 (LAT) X 射线手动测量的。临床医生首先在每个椎骨上找到四个标志，然后从标志延伸线并按照规则测量科布角。整个过程耗时且主观，因此需要自动科布角估计来实现高效可靠的科布角测量。X射线中的噪声和椎骨遮挡是自动Cobb角估计的主要困难，并且利用同一患者的多视图X射线之间的信息具有挑战性。针对这些问题，在本文中，我们提出了一种名为 MPF-net 的有效框架，通过使用深度学习方法进行自动 Cobb 角估计。我们基于主干卷积神经网络结合了椎骨检测分支和地标预测分支，可以为地标预测提供有界区域。然后我们提出了一个提议相关模块来利用相邻椎骨之间的信息，以便我们可以在 LAT X 射线上找到被胸腔和手臂隐藏的椎骨。我们还设计了一个特征融合模块，以利用 AP 和 LAT X 射线中的信息来获得更好的性能。在 2738 对 X 射线上的实验结果表明，我们提出的 MPF-net 实现了精确的椎骨检测和界标预测性能，并且我们在 AP 和 LAT X 射线上分别获得了令人印象深刻的 3.52 和 4.05 圆平均绝对误差，这是更好的比以前的方法。因此，我们可以为临床医生提供自动化、高效、可靠的科布角测量。