Abstract
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.
Highlights
Quantitative phase imaging (QPI) has proved to be a powerful tool for label-free live cell microscopy
PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics, and with 10% fetal bovine serum (FBS)
U-Net based approaches were compared to non-deep learning (DL) approaches
Summary
Quantitative phase imaging (QPI) has proved to be a powerful tool for label-free live cell microscopy. Ongoing progress in QPI microscopy enables the time-lapse observation of subtle changes in the quantitative phase dynamics of cells, such as cell dry mass distribution. [3,4]) that QPI-measured dynamical changes of various parameters are typical for specific cell behaviour and can be used in different applications, e.g., cell motility assessment, homogeneity of cell content or cell mass distribution evaluation These phase-related changes can be observed without fixation, labelling, or cell harvesting, which might severely change cell characteristics [4]
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