3D reconstruction of proximal femoral fracture from biplanar radiographs with fractural representative learning

A femoral fracture is a severe injury occurring in traumatic and pathologic causes. Diagnosis and Preoperative planning are indispensable procedures relying on preoperative radiographs such as X-ray and CT images. Nevertheless, CT imaging has a higher cost, radiation dose, and longer acquisition time than X-ray imaging. Thus, the fracture 3D reconstruction from X-ray images had been needed and remains a challenging problem, as well as a lack of dataset. This paper proposes a 3D proximal femoral fracture reconstruction from biplanar radiographs to improve the 3D visualization of bone fragments during preoperative planning. A novel Fracture Reconstruction Network (FracReconNet) is proposed to retrieve the femoral bone shape with fracture details, including the 3D Reconstruction Network (3DReconNet), novel Auxiliary class (AC), and Fractural augmentation (FA). The 3D reconstruction network applies a deep learning-based, fully Convolutional Network with Feature Pyramid Network architecture. Specifically, the auxiliary class is proposed, which refers to fracture representation. It encourages network learning to reconstruct the fracture. Since the samples are scarce to acquire, the fractural augmentation is invented to enlarge the fracture training samples and improve reconstruction accuracy. The evaluation of FracReconNet achieved a mIoU of 0.851 and mASSD of 0.906 mm. The proposed FracReconNet’s results show fracture detail similar to the real fracture, while the 3DReconNet cannot offer.