Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achieved promising cross-modality medical image segmentation by transferring knowledge from a label-rich source domain to an unlabeled target domain. However, it is also difficult to collect annotations from the source domain in many clinical applications, rendering most prior works suboptimal with the label-scarce source domain, particularly for few-shot scenarios, where only a few source labels are accessible. To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance. In our framework, hallucination and segmentation models are jointly trained with the gradient-based meta-learning strategy to synthesize examples that lead to good segmentation performance on the target domain. To further facilitate data hallucination and cross-domain knowledge transfer, we develop a self-ensembling model with a hallucination-consistent property. Our meta-hallucinator can seamlessly collaborate with the meta-segmenter for learning to hallucinate with mutual benefits from a combined view of meta-learning and self-ensembling learning. Extensive studies on MM-WHS 2017 dataset for cross-modality cardiac segmentation demonstrate that our method performs favorably against various approaches by a lot in the few-shot UDA scenario.

翻译：域偏移和标签稀缺严重限制了深度学习在各类医学图像分析任务中的应用。无监督域适应技术通过从标签丰富的源域向无标签目标域迁移知识，近期在跨模态医学图像分割中取得了显著进展。然而，在众多临床应用中，从源域收集标注同样困难，这导致大多数现有方法在标签稀缺的源域中表现欠佳，尤其在仅能获取少量源域标签的少样本场景下。为实现高效的少样本跨模态分割，我们提出一种新型变换一致性元幻觉框架——元幻觉器，其目标在于学习多样化数据分布并生成有效样本以增强跨模态性能。在该框架中，幻觉模型与分割模型通过基于梯度的元学习策略进行联合训练，以合成能提升目标域分割性能的样本。为进一步促进数据幻觉与跨域知识迁移，我们开发了具有幻觉一致性的自集成模型。元幻觉器能够与元分割器无缝协作，通过结合元学习与自集成学习的视角实现互惠互利的幻觉学习。在MM-WHS 2017数据集上针对跨模态心脏分割的广泛研究表明，在少样本无监督域适应场景下，本方法相较各类方法展现出显著性能优势。