This paper investigates an extremely challenging problem, barely-supervised
medical image segmentation (BSS), where the training dataset comprises limited
labeled data with only single-slice annotations and numerous unlabeled images.
Currently, state-of-the-art (SOTA) BSS methods utilize a registration-based
paradigm, depending on image registration to propagate single-slice annotations
into volumetric pseudo labels for constructing a complete labeled set. However,
this paradigm has a critical limitation: the pseudo labels generated by image
registration are unreliable and noisy. Motivated by this, we propose a new
perspective: training a model using only single-annotated slices as the labeled
set without relying on image registration. To this end, we formulate BSS as an
unsupervised domain adaptation (UDA) problem. Specifically, we first design a
novel noise-free labeled data construction algorithm (NFC) for slice-to-volume
labeled data synthesis, which may result in a side effect: domain shifts
between the synthesized images and the original images. Then, a frequency and
spatial mix-up strategy (FSX) is further introduced to mitigate the domain
shifts for UDA. Extensive experiments demonstrate that our method provides a
promising alternative for BSS. Remarkably, the proposed method with only one
labeled slice achieves an 80.77% dice score on left atrial segmentation,
outperforming the SOTA by 61.28%. The code will be released upon the
publication of this paper.

本论文提出了一种仅使用有限的标注数据和大量未标注图像的单张标注数据训练模型而不依赖图像配准的几乎无监督医学图像分割（BSS）方法，采用无噪声标注数据构建算法和频率空间混合策略，用于减轻数据间的领域转移问题，实验证明该方法对 BSS 具有很高的性能。