Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation

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 Highlights Four strategies for instance cell segmentation with U-Net were compared. Specialised post-processing pipelines with tunable/optimizable parameters were designed for each segmentation strategy. Transferability to different cell types by optimisation of post-processing parameters was tested. The proposed self-supervised pretraining method improved both segmentation performance and transferability to different cell types. A new manually labelled quantitative phase imaging dataset for cell segmentation with unlabelled data for self-supervised pretraining was created.