Deep learning models have become the mainstream method for medical image segmentation, but they require a large manually labeled dataset for training and are difficult to extend to unseen categories. Few-shot segmentation(FSS) has the potential to address these challenges by learning new categories from a small number of labeled samples. The majority of the current methods employ a prototype learning architecture, which involves expanding support prototype vectors and concatenating them with query features to conduct conditional segmentation. However, such framework potentially focuses more on query features while may neglect the correlation between support and query features. In this paper, we propose a novel self-supervised few shot medical image segmentation network with Cross-Reference Transformer, which addresses the lack of interaction between the support image and the query image. We first enhance the correlation features between the support set image and the query image using a bidirectional cross-attention module. Then, we employ a cross-reference mechanism to mine and enhance the similar parts of support features and query features in high-dimensional channels. Experimental results show that the proposed model achieves good results on both CT dataset and MRI dataset.

翻译：深度学习模型已成为医学图像分割的主流方法，但此类模型需要大量人工标注数据集进行训练，且难以推广至未见类别。少样本分割通过从少量标注样本中学习新类别，有望解决上述挑战。现有方法大多采用原型学习架构，通过扩展支持原型向量并将其与查询特征拼接来执行条件分割。然而，此类框架可能更关注查询特征，而忽略了支持特征与查询特征之间的相关性。本文提出一种基于交叉参考Transformer的新型自监督少样本医学图像分割网络，解决了支持图像与查询图像之间缺乏交互的问题。首先利用双向交叉注意力模块增强支持集图像与查询图像的相关性特征，随后引入交叉参考机制来挖掘并增强高维通道中支持特征与查询特征的相似部分。实验结果表明，该模型在CT数据集和MRI数据集上均取得了良好效果。