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Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net

机译：使用MVC-NET自动综合青少年特发性脊柱侧凸评估

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Automated quantitative estimation of spinal curvature is an important task for the ongoing evaluation and treatment planning of Adolescent Idiopathic Scoliosis (AIS). It solves the widely accepted disadvantage of manual Cobb angle measurement (time-consuming and unreliable) which is currently the gold standard for AIS assessment. Attempts have been made to improve the reliability of automated Cobb angle estimation. However, it is very challenging to achieve accurate and robust estimation of Cobb angles due to the need for correctly identifying all the required vertebrae in both Anterior-posterior (AP) and Lateral (LAT) view x-rays. The challenge is especially evident in LAT x-ray where occlusion of vertebrae by the ribcage occurs. We therefore propose a novel Multi-View Correlation Network (MVC-Net) architecture that can provide a fully automated end-to-end framework for spinal curvature estimation in multi-view (both AP and LAT) x-rays. The proposed MVC-Net uses our newly designed multi-view convolution layers to incorporate joint features of multi-view x-rays, which allows the network to mitigate the occlusion problem by utilizing the structural dependencies of the two views. The MVC-Net consists of three closely-linked components: (1) a series of X-modules for joint representation of spinal structure (2) a Spinal Landmark Estimator network for robust spinal landmark estimation, and (3) a Cobb Angle Estimator network for accurate Cobb Angles estimation. By utilizing an iterative multi-task training algorithm to train the Spinal Landmark Estimator and Cobb Angle Estimator in tandem, the MVC-Net leverages the multi-task relationship between landmark and angle estimation to reliably detect all the required vertebrae for accurate Cobb angles estimation. Experimental results on 526 x-ray images from 154 patients show an impressive 4.04 degrees Circular Mean Absolute Error (CMAE) in AP Cobb angle and 4.07 degrees CMAE in LAT Cobb angle estimation, which demonstrates the MVC-Net's capability of robust and accurate estimation of Cobb angles in multi-view x-rays. Our method therefore provides clinicians with a framework for efficient, accurate, and reliable estimation of spinal curvature for comprehensive AIS assessment. (C) 2018 Elsevier B.V. All rights reserved.
机译：脊柱曲率的自动定量估计是对青少年特发性脊柱侧凸（AIS）的持续评估和治疗计划的重要任务。它解决了手动COBB角度测量的广泛接受的缺点（耗时和不可靠），目前是AIS评估的金标准。已经尝试提高自动COBB角估计的可靠性。然而，由于需要正确地识别前后（AP）和横向（LAT）视图X射线的所有所需椎骨而获得精确且稳健地估计Cobb角度是非常具有挑战性的。在LAT X射线中挑战尤其明显，其中通过纹体闭塞椎骨闭塞。因此，我们提出了一种新的多视图相关网络（MVC-NET）架构，其可以为多视图（AP和LAT）X射线中的脊柱曲率估计提供全自动端到端框架。所提出的MVC-Net使用我们的新设计的多视图卷积层来包含多视图X射线的联合特征，这允许网络通过利用两个视图的结构依赖性来减轻遮挡问题。 MVC-NET由三个紧密联系的组件组成：（1）一系列X模块，用于脊柱结构的联合表示（2）脊柱地标估计网络，用于强大的脊柱地标估计，以及（3）COBB角估计网络用于精确的Cobb角度估计。通过利用迭代的多任务培训算法在串联中训练脊柱地标估计器和COBB角估计器，MVC-NET利用地标和角度估计之间的多任务关系来利用以可靠地检测所有所需的椎骨以获得精确的COBB角度估计。来自154名患者的526次X射线图像的实验结果显示了令人印象深刻的4.04度圆形平均绝对误差（CMAE），在LAT COBB角度估计中为4.07摄氏度，展示了MVC-NET的鲁棒能力和准确估算的能力Cobb角度在多视图X射线中。因此，我们的方法为临床医生提供了一种框架，用于综合AIS评估的高效，准确，可靠地估计脊柱曲率。 （c）2018 Elsevier B.v.保留所有权利。

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