  Medical image segmentation typically demands extensive dense annotations for
model training, which is both time-consuming and skill-intensive. To mitigate
this burden, exemplar-based medical image segmentation methods have been
introduced to achieve effective training with only one annotated image. In this
paper, we introduce a novel Cross-model Mutual learning framework for
Exemplar-based Medical image Segmentation (CMEMS), which leverages two models
to mutually excavate implicit information from unlabeled data at multiple
granularities. CMEMS can eliminate confirmation bias and enable collaborative
training to learn complementary information by enforcing consistency at
different granularities across models. Concretely, cross-model image
perturbation based mutual learning is devised by using weakly perturbed images
to generate high-confidence pseudo-labels, supervising predictions of strongly
perturbed images across models. This approach enables joint pursuit of
prediction consistency at the image granularity. Moreover, cross-model
multi-level feature perturbation based mutual learning is designed by letting
pseudo-labels supervise predictions from perturbed multi-level features with
different resolutions, which can broaden the perturbation space and enhance the
robustness of our framework. CMEMS is jointly trained using exemplar data,
synthetic data, and unlabeled data in an end-to-end manner. Experimental
results on two medical image datasets indicate that the proposed CMEMS
outperforms the state-of-the-art segmentation methods with extremely limited
supervision.
