  Although deep learning (DL) shows powerful potential in cell segmentation
tasks, it suffers from poor generalization as DL-based methods originally
simplified cell segmentation in detecting cell membrane boundary, lacking
prominent cellular structures to position overall differentiating. Moreover,
the scarcity of annotated cell images limits the performance of DL models.
Segmentation limitations of a single category of cell make massive practice
difficult, much less, with varied modalities. In this paper, we introduce a
novel semi-supervised cell segmentation method called Multi-Microscopic-view
Cell semi-supervised Segmentation (MMCS), which can train cell segmentation
models utilizing less labeled multi-posture cell images with different
microscopy well. Technically, MMCS consists of Nucleus-assisted global
recognition, Self-adaptive diameter filter, and Temporal-ensembling models.
Nucleus-assisted global recognition adds additional cell nucleus channel to
improve the global distinguishing performance of fuzzy cell membrane boundaries
even when cells aggregate. Besides, self-adapted cell diameter filter can help
separate multi-resolution cells with different morphology properly. It further
leverages the temporal-ensembling models to improve the semi-supervised
training process, achieving effective training with less labeled data.
Additionally, optimizing the weight of unlabeled loss contributed to total loss
also improve the model performance. Evaluated on the Tuning Set of NeurIPS 2022
Cell Segmentation Challenge (NeurIPS CellSeg), MMCS achieves an F1-score of
0.8239 and the running time for all cases is within the time tolerance.

Image and Video Processing;Computer Vision and Pattern Recognition