论文标题
使用实例分割的青少年特发性脊柱侧弯的自动化COBB角度测量
Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
论文作者
论文摘要
脊柱侧弯是脊柱的三维畸形，通常在儿童期被诊断出。它影响了2-3％的人口，北美约有700万人。当前，评估脊柱侧弯的参考标准是基于曲率中心部位的Cobb角度的手动分配。此手动过程耗时且不可靠，因为它受到观察者间和观察者内差异的影响。为了克服这些不准确性，可以使用机器学习（ML）方法来自动化COBB角度测量过程。本文建议使用实例分割模型yolact解决COBB角度测量任务。所提出的方法首先使用Yolact将椎骨在X射线图像中片段段，然后使用最小边界框方法跟踪重要地标。最后，提取的地标用于计算相应的COBB角度。该模型达到了对称的绝对百分比误差（SMAPE）分数为10.76％，证明了该过程在椎骨定位和COBB角度测量中的可靠性。
Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time consuming and unreliable as it is affected by inter- and intra-observer variance. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model. The proposed method first segments the vertebrae in an X-Ray image using YOLACT, then it tracks the important landmarks using the minimum bounding box approach. Lastly, the extracted landmarks are used to calculate the corresponding Cobb angles. The model achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%, demonstrating the reliability of this process in both vertebra localization and Cobb angle measurement.
