北京生物医学工程2024，Vol.43Issue(6)：566-574,9.DOI:10.3969/j.issn.1002-3208.2024.06.003
一种基于改进的AFFormer网络的脊柱椎体分割方法
A spinal vertebral segmentation method based on an improved AFFormer network
摘要
Abstract
Objective To propose an improved AFFormer model for accurate segmentation of spinal vertebrae,which assists physicians in quickly diagnosing scoliosis.Methods The dataset in this article is a full spine orthopedic X-ray,with an image size of approximately 5 000×8 000.Considering the characteristics of large image size,small foreground area,large background area,and large individual differences among subjects but small differences in the same part,the light-weight semantic segmentation model AFFormer is used for spinal segmentation.In response to the phenomenon of losing a large amount of detail information in deep feature maps,when modeling local details in features using pixel semantics,a branch output 8-channel feature map is concatenated on the basis of the original 16 dimensional feature map to achieve multi-scale feature fusion,thereby learning more detail information.The dataset consists of 146 clinical orthopedic full spine X-ray images from a certain hospital.After pixel level annotation using the labelme tool,the images are randomly divided into a training set(117 images),a validation set(15 images),and a testing set(14 images)in an 8∶1∶1 ratio.When training the network,the weighted sum of cross entropy,Dice coefficient,and a custom score function that adds prior knowledge is used as the loss function to optimize model training.On the validation set,we use the average intersection to union ratio and average accuracy for testing,further adjust the hyperparameters of the model,and preliminarily evaluate the models to select the best performing model.Results The model trained using the proposed method is tested on the test set and achieves the highest mIoU value(0.867 8)and mAcc value(0.923 2).Conclusions The method proposed in this article has been experimentally proven to have better segmentation performance than existing mainstream segmentation models,and can achieve precise segmentation of the spinal vertebrae,providing a solid foundation for assisting in spinal medical diagnosis.关键词
脊柱分割/辅助医疗/Transformer结构/多尺度特征Key words
spinal segmentation/auxiliary medical treatment/Transformer structure/multi-scale feature分类
医药卫生引用本文复制引用
王玉婷,张新峰,郭伟,李相生,刘晓民..一种基于改进的AFFormer网络的脊柱椎体分割方法[J].北京生物医学工程,2024,43(6):566-574,9.基金项目
北京市自然科学基金-海淀原始创新联合基金(重点研究专题)(L222018)资助 （重点研究专题）
