Detection of Medial Epicondyle Avulsion in Elbow Ultrasound Images via Bone Structure Reconstruction
This addresses a specific medical imaging problem for diagnosing elbow injuries in baseball players, with incremental improvements over existing methods.
The study tackled detecting medial epicondyle avulsion in elbow ultrasound images by training a reconstruction-based framework on normal cases, achieving pixel-wise and image-wise AUCs of 0.965 and 0.967, respectively.
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.