Detection of Medial Epicondyle Avulsion in Elbow Ultrasound Images via Bone Structure Reconstruction

This study proposes a reconstruction-based framework for detecting medial epicondyle avulsion in elbow ultrasound images, trained exclusively on normal cases. Medial epicondyle avulsion, commonly observed in baseball players, involves bone detachment and deformity, often appearing as discontinuities in bone contour. Therefore, learning the structure and continuity of normal bone is essential for detecting such abnormalities. To achieve this, we propose a masked autoencoder-based, structure-aware reconstruction framework that learns the continuity of normal bone structures. Even in the presence of avulsion, the model attempts to reconstruct the normal structure, resulting in large reconstruction errors at the avulsion site. For evaluation, we constructed a novel dataset comprising normal and avulsion ultrasound images from 16 baseball players, with pixel-level annotations under orthopedic supervision. Our method outperformed existing approaches, achieving a pixel-wise AUC of 0.965 and an image-wise AUC of 0.967. The dataset is publicly available at: https://github.com/Akahori000/Ultrasound-Medial-Epicondyle-Avulsion-Dataset.

Measurement of Medial Elbow Joint Space using Landmark Detection

Ultrasound imaging of the medial elbow is crucial for the early identification of Ulnar Collateral Ligament (UCL) injuries. Specifically, measuring the elbow joint space in ultrasound images is used to assess the valgus instability of elbow. To automate this measurement, a precisely annotated dataset is necessary; however, no publicly available dataset has been proposed thus far. This study introduces a novel ultrasound medial elbow dataset for measuring joint space to diagnose Ulnar Collateral Ligament (UCL) injuries. The dataset comprises 4,201 medial elbow ultrasound images from 22 subjects, with landmark annotations on the humerus and ulna. The annotations are made precisely by the authors under the supervision of three orthopedic surgeons. We evaluated joint space measurement methods using our proposed dataset with several landmark detection approaches, including ViTPose, HRNet, PCT, YOLOv8, and U-Net. In addition, we propose using Shape Subspace (SS) for landmark refinement in heatmap-based landmark detection. The results show that the mean Euclidean distance error of joint space is 0.116 mm when using HRNet. Furthermore, the SS landmark refinement improves 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 and associated risks, which could be leveraged in large-scale screening. Lastly, we demonstrate point-based segmentation of the humerus and ulna using the detected landmarks as input. The dataset will be made publicly available upon acceptance of this paper at: https://github.com/Akahori000/Ultrasound-Medial-Elbow-Dataset.

Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor

We propose an effective unsupervised 3D point cloud novelty detection approach, leveraging a general point cloud feature extractor and a one-class classifier. The general feature extractor consists of a graph-based autoencoder and is trained once on a point cloud dataset such as a mathematically generated fractal 3D point cloud dataset that is independent of normal/abnormal categories. The input point clouds are first converted into latent vectors by the general feature extractor, and then one-class classification is performed on the latent vectors. Compared to existing methods measuring the reconstruction error in 3D coordinate space, our approach utilizes latent representations where the shape information is condensed, which allows more direct and effective novelty detection. We confirm that our general feature extractor can extract shape features of unseen categories, eliminating the need for autoencoder re-training and reducing the computational burden. We validate the performance of our method through experiments on several subsets of the ShapeNet dataset and demonstrate that our latent-based approach outperforms the existing methods.