Multidimensional perturbed consistency learning for semi-supervised medical image segmentation

In this article, we propose a novel multidimensional perturbed consistency network (MPCNet) for more accurate semi-supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture. This type of perturbation can disrupt the propagation process of abstract features over a wide range, enabling the model to learn the distribution of comprehensive data. In addition, we design a shared encoder to extract multi-scale features. After subjecting these features to multidimensional perturbation, a consistency constraint is applied between different results output by three independent decoders. This constraint aims to minimize the statistical differences between these results and effectively leverage unlabeled data. Experimental results on the public LA, Pancreas-CT and ACDC datasets demonstrate that our method outperforms recent SOTA semi-supervised learning methods in terms of various metrics. Our code is released publicly at .