Study on automatic and rapid diagnosis of distal radius fracture by X-ray

本文旨在将深度学习与图像分析技术进行结合，提出一种有效的桡骨远端骨折类型的分类方法。首先，使用扩展U-Net三层级联分割网络，对识别骨折最重要的关节面区和非关节面区进行精准分割；然后，对关节面区和非关节面区图像再分别进行骨折识别；最后，综合判断出正常或者ABC骨折分型结果。实验表明，正常、A型、B型和C型骨折在测试集上的准确率为0.99、0.92、0.91和0.82，而骨科医学专家的平均识别准确率为0.98、0.90、0.87和0.81。所提自动识别方法整体好于专家，在无专家参与的场景下，可以使用该方法进行初步的桡骨远端骨折辅助诊断。.; This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.