PE-MT: A Perturbation-Enhanced Mean Teacher for Semi-Supervised Image Segmentation

The accurate segmentation of medical images is of great importance in many clinical applications and is generally achieved by training deep learning networks on a large number of labeled images. However, it is very hard to obtain enough labeled images. In this paper, we develop a novel semi-supervised segmentation method (called PE-MT) based on the uncertainty-aware mean teacher (UA-MT) framework by introducing a perturbation-enhanced exponential moving average (pEMA) and a residual-guided uncertainty map (RUM) to enhance the performance the student and teacher models. The former is used to alleviate the coupling effect between student and teacher models in the UA-MT by adding different weight perturbations to them, and the latter can accurately locate image regions with high uncertainty via a unique quantitative formula and then highlight these regions effectively in image segmentation. We evaluated the developed method by extracting four different cardiac regions from the public LASC and ACDC datasets. The experimental results showed that our developed method achieved an average Dice similarity coefficient (DSC) of 0.6252 and 0.7836 for four object regions when trained on 5% and 10% labeled images, respectively. It outperformed the UA-MT and can compete with several existing semi-supervised learning methods (e.g., SASSNet and DTC).