The success of a fully supervised method in cardiac Magnetic Resonance Imaging(MRI)segmentation tasks relies on large-scale labeled datasets.However,owing to problems associated with patient privacy and difficulty in manual labeling,the small scale of cardiac MRI annotation data makes the fully supervised method challenging.Based on the semisupervised contrastive learning method,a cardiac MRI segmentation network,CPCL-Net,with double-branch coding and single-branch decoding is developed,and the joint contrastive loss of images and pixels is introduced to improve the ability of the model to express the features of the data samples.To enhance the segmentation accuracy of CPCL-Net for Hard negative samples,a Dynamic Adaptive Weighting Module(DAWM)is developed,and the training contribution at the sample and pixel levels is evaluated using the generated and weight factors,which considerably improves the segmentation accuracy of the model.Experimental results based on the Automated Cardiac Diagnosis Challenge(ACDC)dataset indicate that the network model can obtain high segmentation accuracy with only a small number of annotated samples,which alleviates the problem of low segmentation accuracy caused by insufficient high-quality annotated samples of cardiac MRI.The segmentation accuracies for the left ventricle,right ventricle,and myocardium of the heart are 86.17％,85.52％,and 84.55％,respectively,under the same annotation sample size.The accuracy values are greater than those of the four existing semi-supervised segmentation models and the classical contrastive learning framework,Sim-CLR,which effectively alleviates the dependence and overfitting of the fully supervised model on the sample size.
关键词
对比学习/动态自适应加权/医学图像分割/心脏磁共振成像/联合损失函数
Key words
contrastive learning/dynamic adaptive weighting/medical image segmentation/cardiac Magnetic Resonance Imaging(MRI)/joint loss function
维普
万方数据