arXiv subjects

Petra Paiè

Publications and source records attributed to Petra Paiè.

3 recordsLinked to original sources

arXiv2026

STEPIC: High-Speed Imaging via Spatio-Temporal Encoding in Photonic Integrated Circuits

High-speed imaging of cells in flow is essential for probing cellular heterogeneity in large populations. Existing imaging approaches based on single-pixel detection and spatio-temporal encoding provide exceptional speed, but typically rely on bulky free-space optics, long dispersive elements, and are prone to alignment instabilities. Here, we introduce STEPIC Microscopy, the first fully integrated on-chip system for high-speed imaging via spatio-temporal encoding in photonic integrated circuits. Our platform leverages waveguides, splitters, fiber delay-lines, and 3D optical remappers to encode spatial information into the temporal domain, enabling robust image reconstruction of cells flowing through microchannels. The monolithic architecture provides a compact and robust platform for high-throughput bioimaging, enabling scalable and practical implementations of ultrafast imaging systems.

physics.optics↗

arXiv2025

Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells

Circulating tumor cells (CTCs) are crucial biomarkers in liquid biopsy, offering a noninvasive tool for cancer patient management. However, their identification remains particularly challenging due to their limited number and heterogeneity. Labeling samples for contrast limits the generalization of fluorescence-based methods across different hospital datasets. Analyzing single-cell images enables detailed assessment of cell morphology, subcellular structures, and phenotypic variations, often hidden in clustered images. Developing a method based on bright-field single-cell analysis could overcome these limitations. CTCs can be isolated using an unbiased workflow combining Parsortix technology, which selects cells based on size and deformability, with DEPArray technology, enabling precise visualization and selection of single cells. Traditionally, DEPArray-acquired digital images are manually analyzed, making the process time-consuming and prone to variability. In this study, we present a Deep Learning-based classification pipeline designed to distinguish CTCs from leukocytes in blood samples, aimed to enhance diagnostic accuracy and optimize clinical workflows. Our approach employs images from the bright-field channel acquired through DEPArray technology leveraging a ResNet-based CNN. To improve model generalization, we applied three types of data augmentation techniques and incorporated fluorescence (DAPI) channel images into the training phase, allowing the network to learn additional CTC-specific features. Notably, only bright-field images have been used for testing, ensuring the model's ability to identify CTCs without relying on fluorescence markers. The proposed model achieved an F1-score of 0.798, demonstrating its capability to distinguish CTCs from leukocytes. These findings highlight the potential of DL in refining CTC analysis and advancing liquid biopsy applications.

eess.IV↗

arXiv2022

Superscreening and polarization control in confined ferroelectric nematic liquids

The combination of large spontaneous polarization and fluidity makes the newly discovered ferroelectric nematic liquid crystalline phase (NF) responsive to electric fields in ways that have no counterpart in other materials. We probe this sensitive field response by confining a NF fluid in microchannels that connect electrodes through straight and curved paths. We find that by applying electric fields as low as E c.a. 0.5 V/mm, the NF phase orders with its polarization smoothly following the winding paths of the channels even when oriented antiparallel to the line connecting positive to negative electrodes, implying analogous behavior of the electric field. Upon inversion of E, the polar order undergoes a complex multistage switching process dominated by electrostatic interactions. Multistage polarization switching dynamics is also found in numerical simulations of a quasi-2D continuum model of NF liquid crystals in microchannels, which also clarify the conditions under which the electric field is guided by the microchannels. Experiments and theory indicate that all observations are direct consequences of the prompt effective screening of electric field components normal to the channel walls. This electric "superscreening" effect emerges as a distinctive property of the NF phase, capable of inducing conditions in which both the polarization and the electric field are guided by microchannels.

cond-mat.soft↗