MRI Image Segmentation by Weakly Supervised Learning and Conditional Random Field
Aiming at the high labor costs and low efficiency of traditional cardiac magnetic reso-nance imaging(MRI)segmentation,a cardiac MRI image segmentation method based on weakly super-vised learning and conditional random field is proposed.A dual branch structure is designed,which is supervised by partial cross-entropy loss to learn from existing scribble labels.In order to obtain more precise supervisory signals than scribble labeling,use the outputs of two decoders to enhance model training and dynamically mix the outputs of the two branches to generate pseudo labels.Then,train the segmentation network by combining scribble supervision and pseudo label supervision.Finally,intro-duce conditional random field for post-processing the output of the segmentation network,utilizing pixel relationships to enhance the accuracy of the segmentation results.The experiment results show that this method outperforms existing weakly supervised segmentation methods with similar annotation costs.
medical image segmentationcardiac magnetic resonance imagingweakly supervised learningscribble annotationinquiry experiment
万方数据
维普
