Medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with manual annotations. However, manual annotation is expensive in the field of medical image analysis, which requires domain-specific expertise. To address this challenge, few-shot learning has the potential to learn new classes from only a few examples. In this work, we propose a novel framework for few-shot medical image segmentation, termed CAT-Net, based on cross masked attention Transformer. Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features. We further design an iterative refinement framework that refines the query image segmentation iteratively and promotes the support feature in turn. We validated the proposed method on three public datasets: Abd-CT, Abd-MRI, and Card-MRI. Experimental results demonstrate the superior performance of our method compared to state-of-the-art methods and the effectiveness of each component. we will release the source codes of our method upon acceptance.

翻译：医学图像分割近年来取得了显著进展。基于深度学习的方法被认为是数据密集型技术，需要大量带有手动标注的数据。然而，在医学图像分析领域，手动标注成本高昂，需要领域特定的专业知识。为应对这一挑战，少样本学习具有从少量示例中学习新类别的潜力。在本工作中，我们提出了一种基于交叉掩码注意力Transformer的新型少样本医学图像分割框架，名为CAT-Net。我们提出的网络挖掘支持图像与查询图像之间的相关性，将其限制在仅关注有用的前景信息上，并提升支持原型和查询特征的表示能力。我们进一步设计了一个迭代优化框架，该框架迭代地优化查询图像分割，并反过来促进支持特征。我们在三个公共数据集上验证了所提出方法：Abd-CT、Abd-MRI和Card-MRI。实验结果表明，与最先进的方法相比，我们的方法具有优越的性能，且每个组件均有效。我们将在论文被接收后发布所提出方法的源代码。