Ultrasound imaging of the medial elbow is crucial for the early diagnosis of Ulnar Collateral Ligament (UCL) injuries. Specifically, measuring the elbow joint space in ultrasound images is used to assess the valgus instability of the elbow caused by UCL injuries. To automate this measurement, a model trained on a precisely annotated dataset is necessary; however, no publicly available dataset exists to date. This study introduces a novel ultrasound medial elbow dataset to measure the joint space. The dataset comprises 4,201 medial elbow ultrasound images from 22 subjects, with landmark annotations on the humerus and ulna, based on the expertise of three orthopedic surgeons. We evaluated joint space measurement methods on our proposed dataset using heatmap-based, regression-based, and token-based landmark detection methods. While heatmap-based landmark detection methods generally achieve high accuracy, they sometimes produce multiple peaks on a heatmap, leading to incorrect detection. To mitigate this issue and enhance landmark localization, we propose Shape Subspace (SS) landmark refinement by measuring geometrical similarities between the detected and reference landmark positions. The results show that the mean joint space measurement error is 0.116 mm when using HRNet. Furthermore, SS landmark refinement can reduce the mean absolute error of landmark positions by 0.010 mm with HRNet and by 0.103 mm with ViTPose on average. These highlight the potential for high-precision, real-time diagnosis of UCL injuries by accurately measuring joint space. Lastly, we demonstrate point-based segmentation for the humerus and ulna using the detected landmarks as inputs. Our dataset will be publicly available at https://github.com/Akahori000/Ultrasound-Medial-Elbow-Dataset

翻译：肘关节内侧超声成像对于尺侧副韧带损伤的早期诊断至关重要。具体而言，测量超声图像中的肘关节间隙可用于评估由尺侧副韧带损伤引起的肘关节外翻不稳定性。为实现该测量的自动化，需要基于精确标注数据集训练的模型；然而，目前尚无公开可用的数据集。本研究引入了一种用于测量关节间隙的新型肘关节内侧超声数据集。该数据集包含来自22名受试者的4,201张肘关节内侧超声图像，并基于三位骨科医生的专业知识，对肱骨和尺骨进行了关键点标注。我们在所提出的数据集上评估了关节间隙测量方法，使用了基于热图、基于回归和基于令牌的关键点检测方法。虽然基于热图的关键点检测方法通常能达到较高精度，但有时会在热图上产生多个峰值，从而导致错误检测。为缓解此问题并提升关键点定位精度，我们提出通过测量检测到的关键点位置与参考关键点位置之间的几何相似性，进行形状子空间关键点优化。结果表明，使用HRNet时关节间隙测量的平均误差为0.116毫米。此外，形状子空间关键点优化可使HRNet的关键点位置平均绝对误差降低0.010毫米，使ViTPose的平均绝对误差降低0.103毫米。这些结果凸显了通过精确测量关节间隙实现尺侧副韧带损伤高精度实时诊断的潜力。最后，我们展示了以检测到的关键点作为输入，对肱骨和尺骨进行基于点的分割。我们的数据集将在https://github.com/Akahori000/Ultrasound-Medial-Elbow-Dataset 公开提供。