  Medical image segmentation has witnessed significant advancements with the
emergence of deep learning. However, the reliance of most neural network models
on a substantial amount of annotated data remains a challenge for medical image
segmentation. To address this issue, few-shot segmentation methods based on
meta-learning have been employed. Presently, the methods primarily focus on
aligning the support set and query set to enhance performance, but this
approach hinders further improvement of the model's effectiveness. In this
paper, our objective is to propose a few-shot medical segmentation model that
acquire comprehensive feature representation capabilities, which will boost
segmentation accuracy by capturing both local and long-range features. To
achieve this, we introduce a plug-and-play attention module that dynamically
enhances both query and support features, thereby improving the
representativeness of the extracted features. Our model comprises four key
modules: a dual-path feature extractor, an attention module, an adaptive
prototype prediction module, and a multi-scale prediction fusion module.
Specifically, the dual-path feature extractor acquires multi-scale features by
obtaining features of 32{\times}32 size and 64{\times}64 size. The attention
module follows the feature extractor and captures local and long-range
information. The adaptive prototype prediction module automatically adjusts the
anomaly score threshold to predict prototypes, while the multi-scale fusion
prediction module integrates prediction masks of various scales to produce the
final segmentation result. We conducted experiments on publicly available MRI
datasets, namely CHAOS and CMR, and compared our method with other advanced
techniques. The results demonstrate that our method achieves state-of-the-art
performance.
