Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e.g. imaging modalities, institutions and equipment sequences). How to enhance the FSMIS models to generalize well across the different specific medical imaging domains? In this paper, we focus on the matching mechanism of the few-shot semantic segmentation models and introduce an Earth Mover's Distance (EMD) calculation based domain robust matching mechanism for the cross-domain scenario. Specifically, we formulate the EMD transportation process between the foreground support-query features, the texture structure aware weights generation method, which proposes to perform the sobel based image gradient calculation over the nodes, is introduced in the EMD matching flow to restrain the domain relevant nodes. Besides, the point set level distance measurement metric is introduced to calculated the cost for the transportation from support set nodes to query set nodes. To evaluate the performance of our model, we conduct experiments on three scenarios (i.e., cross-modal, cross-sequence and cross-institution), which includes eight medical datasets and involves three body regions, and the results demonstrate that our model achieves the SoTA performance against the compared models.

翻译：少样本医学图像分割（FSMIS）旨在医学图像分析领域实现有限标注数据下的学习。尽管已取得进展，但当前FSMIS模型均在相同数据域上训练与部署，这与医学影像数据常跨越不同数据域（如成像模态、机构及设备序列）的临床现实不符。如何增强FSMIS模型在不同特定医学影像域间的泛化能力？本文聚焦少样本语义分割模型的匹配机制，针对跨域场景提出一种基于地球移动距离（EMD）计算的领域鲁棒匹配机制。具体而言，我们构建前景支持-查询特征间的EMD传输过程，在EMD匹配流程中引入纹理结构感知权重生成方法——通过对节点执行基于Sobel算子的图像梯度计算来抑制领域相关节点。此外，引入点集级距离度量指标以计算从支持集节点到查询集节点的传输代价。为评估模型性能，我们在三种场景（跨模态、跨序列、跨机构）上开展实验，涵盖八个医学数据集及三个身体部位，实验结果表明本模型相较基线模型取得了最先进的性能。