Measurement of Medial Elbow Joint Space using Landmark Detection

2024-12-17

arXiv.org

AI Summary

Ultrasound-based assessment of medial elbow joint instability due to ulnar collateral ligament (UCL) injury is hindered by the absence of publicly available, precisely annotated ultrasound datasets and automated methods for quantitative joint space measurement. Method: We introduce the first open-source, expert-annotated ultrasound dataset of the medial elbow (4,201 images from 22 subjects) and propose a Shape Subspace (SS) landmark refinement framework that leverages geometric similarity to correct detection bias, enabling bone-structure-point-driven real-time segmentation and high-accuracy joint space quantification. Contribution/Results: Integrating SS refinement into state-of-the-art landmark detectors (HRNet, ViTPose) significantly improves performance: HRNet’s mean absolute error (MAE) in joint space measurement drops to 0.116 mm; SS reduces landmark localization MAE by 0.010 mm (HRNet) and 0.103 mm (ViTPose). Both the dataset and source code are publicly released.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

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

Problem

• Automates measurement of medial elbow joint space
• Addresses lack of public ultrasound elbow datasets
• Enhances landmark detection accuracy for UCL diagnosis

Innovation

• Heatmap-based landmark detection
• Shape Subspace landmark refinement
• Point-based segmentation technique