MUST: Mean-teacher inspired and Uncertainty-aware Semi-supervised Transformer for cell instance segmentation

Cell instance segmentation in high-resolution microscopy images remains challenging when dense annotations are limited across diverse imaging modalities. This paper presents MUST, a Mean-teacher inspired and Uncertainty-aware Semi-supervised Transformer for cell instance segmentation under limited target-domain annotations. MUST uses adaptive supervised warm-up to stabilize the student model before activating unlabeled consistency learning, and adopts a two-stream mini-batch design to jointly optimize labeled and unlabeled samples. Instead of relying on hard pseudo-labels, an EMA teacher provides soft probability maps as consistency targets for strongly perturbed student predictions. To reduce error propagation from uncertain teacher predictions, MUST further applies entropy- and region-aware reliability weighting together with dynamic unsupervised loss scaling. During inference, sliding-window prediction and test-time augmentation are integrated to improve robustness on high-resolution microscopy images. On the NeurIPS22-CellSeg benchmark, following supervised pre-training on external annotated datasets and using only 10% of the labeled CellSeg training images for supervised optimization, MUST achieves an Overall F1 score of 0.8878 on the official tuning set, reaching the third-best level among representative topperforming challenge methods. These results demonstrate that MUST provides an effective solution for multi-modality cell instance segmentation with reduced target-domain annotation requirements.