Attention and Frequency-Domain Fusion Network for Detecting Skeleton Edge Points in Ankle Joint Fracture Diagnosis

Ankle fractures are one of the most common types of fractures in clinical practice, and incorrect assessment of joint stability during surgery can easily lead to various ligament injuries and complications such as arthritis. Ultrasonic imaging is currently one of the least invasive and least burdensome methods for diagnosing ankle fractures in patients. However, previous studies mostly diagnosed joint fractures by segmenting the shape and position of the entire bone, while the latest clinical research found that joint stability can be determined by measuring the gap between two segments of bone. Therefore, we propose a new model that accurately detects skeletal edge points on both sides of the ankle joint, measures the length of the bone gap, and thus assists doctors in accurate diagnosis or treatment planning. Our model uses a Region Proposal Network and Transformer encoder as the basic backbone. It first filters the background to obtain rough interesting regions, then collectively models the internal attention between background pixels and target pixels to enhance the distinction between the skeletal main body and background. Additionally, it extends frequency domain recognition to strengthen the differentiation between skeletal main body and background, thereby promoting edge point detection. We use ankle joint ultrasonic images from real patients, annotated by bone disease experts. Experimental results demonstrate that our model can accurately locate the detailed positions of skeletal edge points, even in images containing complex body tissues, ensuring correct detection. Furthermore, In addition, our model can process 24 frames per second and has the ability to track frames in a video.