Center for Orthopaedic Biomechanics, University of Denver, 2155 E Wesley Ave, Denver, CO, 80208, USA.
Department of Electrical and Computer Engineering, University of Denver, 2155 E Wesley Ave, Denver, CO, 80208, USA.
Ann Biomed Eng. 2024 Jun;52(6):1591-1603. doi: 10.1007/s10439-024-03473-5. Epub 2024 Apr 1.
Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0 or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.
从立体射线照相术中对原生解剖结构进行运动学跟踪为评估人体运动提供了定量基础。传统的跟踪程序需要大量的手动工作，并且需要获取和注释特定于主体的体积医学图像。目前的工作介绍了一种从动态立体射线照相术中完全自动跟踪原生膝关节解剖结构的框架，该框架无需依赖体积扫描。该方法包括三个计算步骤。首先，使用卷积神经网络对捕获的射线照片进行注释，并使用解剖学标记。接下来，从注释的标记点制定非凸多项式优化问题，以获取初步的解剖结构和姿势估计。最后，进行全局优化程序以同时改进解剖结构和姿势。最大化一个目标函数，该函数量化掩蔽射线照片和从统计形状和强度模型生成的数字重建射线照片之间的相似性。所提出的框架针对包含动态活动的手动跟踪试验以及捕获静态膝关节模型的附加帧进行了评估。实验表明，在两个评估队列中，解剖表面误差通常低于 1.0 毫米。在两个评估队列中，18 个自由度中的 15 个自由度的个体骨骼姿势估计的中位数绝对误差均低于 1.0 或毫米。结果表明，可以通过显著减少手动工作并避免依赖体积扫描来从立体射线照相术中对原生解剖结构进行准确的姿势估计。
