Solid tumor metastases cause most cancer-related deaths. The prevention of their occurrence misses suitable anti-metastases medicines newly labeled as migrastatics. The first indication of migrastatics potential is based on an inhibition of in vitro enhanced migration of tumor cell lines. Therefore, we decided to develop a rapid test for qualifying the expected migrastatic potential of some drugs for repurposing. The chosen Q-PHASE holographic microscope provides reliable multifield time-lapse recording and simultaneous analysis of the cell morphology, migration, and growth. The results of the pilot assessment of the migrastatic potential exerted by the chosen medicines on selected cell lines are presented.

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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.

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• časopisecké články MeSH