  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.
