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. Code: https://github.com/hust-linyi/CAT-Net.

翻译：近年来，医学图像分割取得了显著进展。基于深度学习的方法被认为是一种数据密集型技术，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，小样本学习具有仅从少量样本中学习新类别的潜力。本文提出了一种新颖的小样本医学图像分割框架，称为CAT-Net，该框架基于交叉掩码注意力Transformer。所提出的网络挖掘支持图像与查询图像之间的相关性，将其限制为仅关注有用的前景信息，并增强支持原型和查询特征的表征能力。我们还设计了一个迭代优化框架，该框架能够迭代地细化查询图像的分割结果，并同时提升支持特征的质量。我们在三个公开数据集（Abd-CT、Abd-MRI和Card-MRI）上验证了所提出的方法。实验结果表明，我们的方法在性能上优于现有最先进方法，并且每个组件均具有有效性。代码：https://github.com/hust-linyi/CAT-Net。