Deep learning-based, misalignment resilient, real-time Fourier Ptychographic Microscopy reconstruction of biological tissue slides
Vittorio Bianco, Mattia Delli Priscoli, Daniele Pirone, Gennaro Zanfardino, Pasquale Memmolo, Francesco Bardozzo, Lisa Miccio, Gioele Ciaparrone, Pietro Ferraro, Roberto Tagliaferri
Fourier ptychographic microscopy probes label free samples from multiple angles
and achieves super resolution phase-contrast imaging according to a synthetic
aperture principle. Thus, it is particularly suitable for high-resolution
imaging of tissue slides over a wide field of view. Recently, in order to make
the optical setup robust against misalignments-inducedartefacts, numerical
multi-look has been added to the conventional phase retrieval process, thus
allowing the elimination of related phase errors but at the cost of a long
computational time. Here we train a generative adversarial network to emulate
the process of complex amplitude estimation. Once trained, the network can
accurately reconstruct in real-time Fourier ptychographic images acquired
using a severely misaligned setup. We benchmarked the network by
reconstructing images of animal neural tissue slides. Above all, we show that
important morphometric information, relevant for diagnosis on neural tissues,
are retrieved using the network output. These are in very good agreement with
the parameters calculated from the ground-truth, thus speeding up significantly
the quantitative phase-contrast analysis of tissue samples.
Models, Code and Data are available under explicit request: Access Repository
How to cite this paper:
Under Review
Neuroblastoma cells classification through learning approaches by direct analysis of digital holograms
Mattia Delli Priscoli, Pasquale Memmolo, Gioele Ciaparrone, Vittorio Bianco, Francesco Merola,Lisa Miccio, Francesco Bardozzo, Daniele Pirone, Martina Mugnano, Flora Cimmino, Mario Capasso,Achille Iolascon, Pietro Ferraro,Roberto Tagliaferri
The label-free single cell analysis by machine and Deep Learning, in combination
with digital holography in transmission microscope configuration, is becoming a
powerful framework exploited for phenotyping biological samples. Usually,
quantitative phase images of cells are retrieved from the reconstructed complex
diffraction patterns and used as inputs of a deep neural network.
However, the phase retrieval process can be very time consuming and prone to
errors. Here we address the classification of cells by using learning strategies
with images coming directly from the raw recorded digital holograms, i.e. without
any data processing or refocusing involved. Indeed, in the raw digital hologram
the entire complex amplitude information of the sample is intrinsically embedded
in the form of modulated fringes. We develop a training strategy, based on deep
and feature based machine learning models, in order extract such information
by skipping the classical reconstruction process for classifying different
neuroblastoma cells. We provided an experimental validation by using the proposed
strategy to classify two neuroblastoma cell lines.
How to cite this paper:
M. Delli Priscoli et al., "Neuroblastoma Cells Classification Through
Learning Approaches by Direct Analysis of Digital Holograms,"
in IEEE Journal of Selected Topics in Quantum Electronics,
vol. 27, no. 5, pp. 1-9, Sept.-Oct. 2021,
Art no. 5500309, doi: 10.1109/JSTQE.2021.3059532.