Authors
Yuwen Zeng, Adel Hafiane, Helene Laurent
Published in
IEEE journal of biomedical and health informatics. Volume PP. Oct 09, 2026. Epub Oct 09, 2026.
Abstract
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.
PMID:
42853747
Bibliographic data and abstract were imported from PubMed on 10 Oct 2026.
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