基于选择监督和动态阈值的半监督医学图像分割模型OA

Mean Teacher is a highly regarded and widely used framework for semi-supervised medical image segmentation.However,methods based on the Mean Teacher do not selectively accept the supervision of the student network over the teacher network during training.This implies that even if the performance of the teacher network is inferior to that of the student network,the student network is still supervised by the teacher network.This results in an accumulation of errors.Moreover,all these methods use a fixed threshold for pseudo-labels to obtain correct information from the predictions of the teacher network.Although this filters out most incorrect information,it also eliminates much of the correct information,which greatly limits the availability of pseudo-labels.To address these issues,a semi-supervised medical image segmentation model based on selective supervision and dynamic threshold,named SSDT,is proposed.This model allows the student network to choose when to accept supervision from the teacher network,preventing the teacher network from supervising the student network when its performance is insufficient.The network can select a pseudo-label threshold suitable for the current training stage using the newly designed dynamic threshold module,thereby maximizing the retention of the correct information in the teacher network output.On the LA and ACDC datasets with 20％labeled data,SSDT achieves Dice coefficients of 90.94％and 89.93％,respectively.Extensive experiments on four medical image datasets demonstrate that SSDT has superior segmentation performance compared with several state-of-the-art methods.