| Autor: |
Cédric Allier, Lionel Hervé, Chiara Paviolo, Ondrej Mandula, Olivier Cioni, William Pierré, Francesca Andriani, Kiran Padmanabhan, Sophie Morales |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Physics, Vol 9 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2296-424X |
| DOI: |
10.3389/fphy.2021.776805 |
| Popis: |
We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. The image reconstruction part of the pipeline features a convolutional neural network performing phase unwrapping and accelerating the inverse problem optimization. It allows phase retrieval at the 4K level (3,840 × 2,748 pixels) in 3 s. The analysis part of the pipeline features a suite of convolutional neural networks estimating different cell metrics from the reconstructed image, that is, cell surface area, cell dry mass, cell length, and cell thickness. The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. The main advantage brought by this method is the fast processing time, that is, the analysis rate of ∼25.000 cells measurements per second. Although our proof of principle has been established with lens-free microscopy, the approach of using quantitative cell representation in a deep learning framework can be similarly applied to other microscopy techniques. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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