用超声图像自动测量肘关节间隙，辅助诊断韧带损伤
Measurement of Medial Elbow Joint Space using Landmark Detection
- 基于三位骨科医生标注的4201张超声图，构建新数据集
- 提出形状子空间精修方法，降低关键点误差0.103mm
- 可实现高精度实时诊断，适合临床与科研使用
肘部内侧超声成像是早期诊断尺侧副韧带（UCL）损伤的关键。通过测量超声图像中的肘关节间隙，可评估因UCL损伤引起的外翻不稳。为实现自动化测量，需在精确标注的数据集上训练模型，但目前尚无公开可用的数据集。本研究构建了一个新的肘部内侧超声数据集，包含22名受试者的4,201张图像，并由三位骨科医生对肱骨和尺骨关键点进行标注。我们评估了热图、回归与基于标记的检测方法在该数据集上的表现。尽管热图法通常精度较高，但常出现多个峰值导致误检。为此，我们提出形状子空间（SS）精修方法，通过几何相似性优化关键点定位。结果显示，使用HRNet时关节间隙测量平均误差为0.116 mm；SS精修使HRNet的点位平均绝对误差降低0.010 mm，ViTPose降低0.103 mm。此外，我们展示了基于检测关键点的肱骨和尺骨分割方法。该数据集将公开发布于https://github.com/Akahori000/Ultrasound-Medial-Elbow-Dataset。
原文摘要 · Abstract (English)
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
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