End-to-end neural network for speeding up the tomographic reconstruction in holographic imaging flow cytometry

Daniele Pirone; Daniele G. Sirico; Lisa Miccio; Vittorio Bianco; Martina Mugnano; Pietro Ferraro; Pasquale Memmolo

doi:10.1117/12.2674981

9 August 2023 End-to-end neural network for speeding up the tomographic reconstruction in holographic imaging flow cytometry

Daniele Pirone, Daniele G. Sirico, Lisa Miccio, Vittorio Bianco, Martina Mugnano, Pietro Ferraro, Pasquale Memmolo

Author Affiliations +

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210Q (2023) https://doi.org/10.1117/12.2674981
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

Cellular populations are often heterogeneous, thus the intraspecies variability is usually lost in the average measurements accessed by conventional measuring devices. For this reason, the single-cell analysis provided by optical microscopy is opening new promising perspectives in biomedicine. The gold standard technique in this context is Fluorescence Imaging Flow Cytometry (FIFC), as big datasets of single cells flowing in suspension through a measuring device can be collected in short times and multiple parameters can be measured at the single-cell level. However, to overcome the limitations related to the staining process, in the recent years Holographic Imaging Flow Cytometry (HIFC) has been proved to be a viable label-free alternative to FIFC, able to give access also to the cell biophysical features. Very recently, the latest evolution of HIFC has been demonstrated, i.e. Tomographic Phase Imaging Flow Cytometry (TPIFC), in which the 3D spatial distribution of the refractive indices of single cells can be reconstructed thanks to their roto-translation along a microfluidic channel. However, to retrieve one single tomogram, hundreds of digital holograms must be converted into the corresponding phase-contrast maps. Currently, this is the actual bottleneck for the high-throughput TPIFC, which aims to a fast 3D analysis of the cellular populations. Therefore, here we show that a fully convolutional end-to-end context aggregation neural network can greatly speed up the phase retrieval process, thus reducing the computational time for the tomographic reconstruction from tens of minutes to few seconds, while providing at the same time high fidelity and small memory footprint.

Conference Presentation

Citation Download Citation

Daniele Pirone, Daniele G. Sirico, Lisa Miccio, Vittorio Bianco, Martina Mugnano, Pietro Ferraro, and Pasquale Memmolo "End-to-end neural network for speeding up the tomographic reconstruction in holographic imaging flow cytometry", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210Q (9 August 2023); https://doi.org/10.1117/12.2674981

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