Semi-Supervised Cervical Spine MRI Segmentation Model in Federated Heterogeneous Data
Segmented medical images for diagnosis serve as an effective auxiliary method in clinical and medical research.However,challenges such as privacy concerns,data dispersion,and labeling difficulties hinder their practical application.For example,in the case of cervical spine Magnetic Resonance Imaging(MRI)image segmentation,it is more challenging to obtain image data,and labeling costs are high.It is difficult for the cervical spine segmentation model to effectively extract detailed cervical spine information when encountering heterogeneous data from different sources.Therefore,in the federated learning scenario,this paper proposes M-FedLO.It is a multi-scale semi-supervised segmentation network based on label separation and bootstrapping.It aims to address the challenges of limited labeled information and reduced accuracy when handling heterogeneous data.M-FedLO employs label separation to separately segment the vertebrae and intervertebral discs while achieving multi-scale outputs.This allows for further edge information extraction and better separation between vertebral blocks and intervertebral discs.In the"global+local"mode of federated learning,the labels from the global model guide the local models.This ensures that the features extracted by the local models from unlabeled data approximate those of the global model.This,in turn,enhances the utilization of unlabeled data by the local models.Additionally,the approach uses Stochastic Weight Averaging(SWA)to optimize the parameters to alleviate model weight oscillation issues and enhance the model's generalization capability.The experimental results demonstrate that the proposed model outperforms the semi-supervised baseline segmentation models in the segmentation of non-heterogeneous cervical spine MRI medical images and heterogeneous cervical spine images.Specifically,compared with the best-performing ICT model on the cervical spine dataset,the proposed approach achieves an improvement in the Dice Similarity Coefficient(DSC)metric to 86.86％.This represents an enhancement by 1.72 percentage points.
万方数据
维普
