In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.
CITATION STYLE
Vicar, T., Chmelik, J., Jakubicek, R., Chmelikova, L., Gumulec, J., Balvan, J., … Kolar, R. (2021). Self-supervised pretraining for transferable quantitative phase image cell segmentation. Biomedical Optics Express, 12(10), 6514. https://doi.org/10.1364/boe.433212
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