Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation

Vicar, T.; Chmelik, J.; Jakubicek, R.; Chmelikova, L.; Gumulec, J.; Balvan, J.; Provaznik, I.; Kolar, R.

doi:10.1101/2021.05.24.445397

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bioRxiv · 10.1101/2021.05.24.445397

Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation

Vicar, T.·Chmelik, J.·Jakubicek, R.·Chmelikova, L.·Gumulec, J.·Balvan, J.·Provaznik, I.·Kolar, R.

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Abstract

In this paper, 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 by 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. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY HighlightsO_LIFour strategies for instance cell segmentation with U-Net were compared. C_LIO_LISpecialised post-processing pipelines with tunable/optimizable parameters were designed for each segmentation strategy. C_LIO_LITransferability to different cell types by optimisation of post-processing parameters was tested. C_LIO_LIThe proposed self-supervised pretraining method improved both segmentation performance and transferability to different cell types. C_LIO_LIA new manually labelled quantitative phase imaging dataset for cell segmentation with unlabelled data for self-supervised pretraining was created. C_LI

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Vicar, T., Chmelik, J., Jakubicek, R., Chmelikova, L., Gumulec, J., Balvan, J., Provaznik, I., Kolar, R.. 2021-05-25. Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation. https://doi.org/10.1101/2021.05.24.445397

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