Integrated Attentional Teacher Mutual Consistency Semi-Supervised Medical Image Segmentation
Medical image segmentation plays a crucial role in disease-assisted diagnosis.Existing deep segmentation models rely on annotated data for large-scale training.Medical image annotation requires clinical doctors with professional backgrounds to perform pixel-level annotation,making it difficult to obtain annotation data.The semi-supervised medical image segmentation method utilizes a small amount of labeled data and a large amount of unlabeled data for learning,which can alleviate the difficulty of obtaining labeled data completely.This study proposes a semi-supervised segmentation network,TCA-Net to address the issue of semi-supervised segmentation models that do not fully utilize learnable information from unlabeled data.This network uses U-Net as the backbone network.This network solves the problem of information loss during downsampling by introducing a Convolutional Block Attention Module(CBAM)and Multi-Head self Attention module(MHA)in the U-Net network.To utilize the uncertainty information in unlabeled data fully,this network employs the construction of a teacher mutual consistency model.The model consists of a student model with an encoder and three slightly different decoders and a teacher model.By adding consistency constraints between the probability mapping of the student model and the pseudo labels of the teacher model,this network succeeds in minimizing the difference between outputs during training,thereby improving the segmentation effect of the model.To verify the effectiveness of the proposed model,experiments are conducted on the publicly available WORD abdominal multi-organ and ACDC heart datasets.On the WORD dataset using 20％labeled data,the Dice coefficient,Jaccard index,HD95,and ASD reach 90.81％,83.79％,21.38,and 6.08,respectively,whereas they reach 89.69％,81.94％,1.66,and 0.45,respectively,on the ACDC dataset.By comparing the results of ablation experiments with those of advanced algorithms,TCA-Net effectively improves the utilization of unlabeled data and achieves good segmentation results on different datasets,verifying the robustness of the model.
medical image segmentationsemi-supervised learningattention mechanismaverage teacher modelconsistent regularization
万方数据
维普
