Classification of Clavicle Fractures based on Multi-View Fusion

Abstract

Abstract Clavicle fracture is a common shoulder injury. Clinical Allman classification divides clavicle fracture into middle fracture, distal fracture and proximal fracture. Different fracture types have corresponding treatment methods and different healing standards. The diagnosis of clavicle fractures can be misdiagnosed and missed by doctors due to blurring of the fracture line. In order to improve the diagnostic efficiency of clinicians and provide clearer treatment ideas, this paper establishes a two-stage clavicle-assisted diagnostic model. The first stage is based on 3D U-Net to segment the shoulder CT of normal clavicle and clavicle fracture in 3D, with dice coefficient reaching 0.9441, and then calculates the two-dimensional image information entropy of the image to select the key layers of the clavicle for classification of the segmented 3D image. The second stage of classification was performed to fuse the key layers data under the three views. The experimental results showed that the three-view fusion had a higher classification accuracy compared to the single-view slice, and the accuracy was improved by 1.3% to 93.4% compared to the best coronal classification, which showed that the two-stage classification method showed good classification effect and could help doctors improve the diagnostic efficiency.