<p>Few-shot segmentation, which aims to segment unseen classes from a small number of labeled images, has made great progress in both natural and medical images in recent years. However, there are still several flaws in the current few-shot medical image segmentation methods. The diversity of the base classes in the training stage is insufficient, so it is still difficult to train a model with strong generalization through a large number of images and classes like natural images. Besides, due to the discrepancies between the query and support images, even if the information with representational is mined from the support images, it is also difficult for this information to exert a stable effect when guiding query image segmentation. To address these issues, we propose a novel method to construct support-query pairs based on superpixel hierarchies. Aim to simulate the variation of the same medical class across different slice sequences, and make the model adapt to the difference between support and query. Furthermore, we also design a pipeline to learn an optimized prototypical network for prediction by leveraging the invariance of grayscale transformation and the equivariance of geometric transformation. Such an operation can improve the prototype guidance in feature space for two query views with different transformations. Extensive experiments on two abdominal medical datasets (MRI and CT) effectively demonstrate the superiority of our network when compared with current state-of-the-art methods.</p>