Self-supervised pretraining for transferable quantitative phase image cell segmentation
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Periodical |
| Magazine / Source | BIOMEDICAL OPTICS EXPRESS |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-10-6514&id=459853 |
| Doi | http://dx.doi.org/10.1364/BOE.433212 |
| Keywords | Self-supervised pretraining; transferable quantitative phase image cell segmentation |
| Description | 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. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement |
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