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18 June 2024 Machine learning-enabled quantitative phase imaging: my collaborations with Prof. Gabi Popescu
Mark A. Anastasio
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Proceedings Volume PC12996, Unconventional Optical Imaging IV; PC1299602 (2024) https://doi.org/10.1117/12.3022992
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
Quantitative phase imaging (QPI) has recently emerged as a potentially valuable label-free approach which, due to its high resolution and sensitivity, has enabled a broad range of new applications. However, being a label-free technique, structures in a QPI image of a cell cannot always be readily identified because the image lacks specificity. To address this, machine learning methods have been deployed to map an acquired QPI image to a different image type, such as a simulated fluorescence image or an image representing a labeled segmentation mask. In this talk, I review several productive collaborations I had with Prof. Gabi Popescu on this topic. The applications surveyed include live-dead assay on unlabeled cells, cell stage classification and artificial confocal microscopy for deep label-free imaging.
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Mark A. Anastasio "Machine learning-enabled quantitative phase imaging: my collaborations with Prof. Gabi Popescu", Proc. SPIE PC12996, Unconventional Optical Imaging IV, PC1299602 (18 June 2024); https://doi.org/10.1117/12.3022992
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KEYWORDS
Image segmentation
Machine learning
Phase imaging
Confocal microscopy
Fluorescence
Image resolution
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