Bone Fracture Visualization and Analysis Using Map Projection and Machine Learning Techniques

Abstract

Understanding intertrochanteric fracture distribution is an important topic in orthopaedics due to its high morbidity and mortality. The intertrochanteric fracture can contain high dimensional information including complicated 3D fracture lines, which often make it difficult to visualize or to obtain valuable statistics for clinical diagnosis and prognosis applications. This paper proposed a map projection technique to map the high dimensional information into a 2D parametric space. This method can preserve the 3D proximal femur surface and structure while visualizing the entire fracture line with a single plot. A total of 100 patients are studied based on the original radiographs acquired by CT scan. The comparison shows that the proposed map projection representation is more efficient and richer in information visualization than the conventional heat map technique. Using the proposed method, a fracture probability can be obtained at any location in the 2D parametric space, from which the most probable fracture region can be accurately identified. Based on the 2D parametric map, the principal component analysis is carried out to investigate the correlations of the fracture lines among different proximal femur regions.