Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image
  Segmentation
Ziyuan Zhao Fangcheng Zhou Zeng Zeng Cuntai Guan S. Kevin Zhou
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.