首页|Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning
Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning
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A semi-supervised convolutional neural network segmentation method of medical images based on contrastive learn-ing is proposed.The cardiac magnetic resonance imaging(MRI)images to be segmented are preprocessed to obtain positive and negative samples by labels.The U-Net shrinks network is applied to extract features of the positive sam-ples,negative samples,and input samples.In addition,an unbalanced contrastive loss function is proposed,which is weighted with the binary cross-entropy loss function to obtain the total loss function.The model is pre-trained with labeled samples,and unlabeled images are predicted by the pre-trained model to generate pseudo-labels.A pseudo-label post-processing algorithm for removing disconnected regions and hole filling of pseudo-labels is pro-posed to guide the training process of semi-supervised networks.The results on the Sunnybrook dataset show that the segmentation results of this model are better,with a higher dice coefficient,accuracy,and recall rate.
ZHU Enrong、ZHAO Haochen、HU Xiaofei
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School of Geography and Bioinformatics,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
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