ArticleBiomedical optics express2021
Self-supervised pretraining for transferable quantitative phase image cell segmentation.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked Autoencoder.Artificial intelligence in medicine · 2024Article
- Primary assessment of medicines for expected migrastatic potential with holographic incoherent quantitative phase imaging.Biomedical optics express · 2023Article
- Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps.Biomedical optics express · 2023Article
- Self-supervised pretraining for transferable quantitative phase image cell segmentation.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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