Discrimination of Fracture Types Based on Neural Network and Voxelized Bone Template

In view of the complex and diverse fracture types which are difficult to identify automatically,a novel method is put forward based on a neural network and a voxelized bone template.Firstly,a re-gion-divided and regular voxel template is constructed to effectively indicate the damaged region with ir-regular morphological structure.Secondly,an isomorphic mapping from a damaged bone to the template is established to extract the voxel information of its fracture region,and a database of fracture types is created accordingly.Next,a kind of constraint relationship between voxels in the damaged area is defined based on the medical prior knowledge,and the continuous damaged areas are considered as units to combine samplesof the same type for augmenting the samples.Finally,a neural network is designed and trained on the aug-mented samples to identify fracture types.The 352 samples of femur fracture are collected in the experiment,and the coincident rate between the predicted result and the clinical diagnosis of orthopedists is 97%.The classification accuracy,time performance and the number of damage types identified are better than the ex-isting methods.Experimental results show that the proposed method can assist doctors to quickly and effec-tively determine the fracture types in patients,and provides a theoretical basis for the selection of internal fixation plate in fracture surgery.