Daniel Pirone
70 Papers
11 Citations
Daniel Pirone is an academic researcher. The author has contributed to research in topics: Computer science & Microscopy. The author has an hindex of 2, co-authored 2 publications.
Chat about Author
Papers
Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry
Daniel Pirone,Joowon Lim,Francesco Merola,Lisa Miccio,Martina Mugnano,Vittorio Bianco,Flora Cimmino,Feliciano Visconte,Anna Montella,Mario Capasso,Achille Iolascon,Pasquale Memmolo,Demetri Psaltis,Pietro Ferraro +13 more
TL;DR: In this paper , a method based on statistical inference was proposed to identify the cell nucleus using a refractive index tomogram of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode.
82
Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning.
Daniel Pirone,Daniele Gaetano Sirico,Lisa Miccio,Vittorio Bianco,Martina Mugnano,Pietro Ferraro,Pasquale Memmolo +6 more
TL;DR: A compact deep convolutional neural network parameterization that can fit into on-chip SRAM and a small memory footprint is accomplished, thus demonstrating its possible exploitation to provide onboard computations for lab-on-chip devices with low processing hardware resources.
60
Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry
Daniel Pirone,Anna Montella,Daniele Gaetano Sirico,Martina Mugnano,Massimiliano M. Villone,Vittorio Bianco,Lisa Miccio,Anna Maria Porcelli,Ivana Kurelac,Mario Capasso,Achille Iolascon,Pier Luca Maffettone,Pasquale Memmolo,Pietro Ferraro +13 more
TL;DR: In this article , a machine learning-powered tomographic phase imaging flow cytometry system capable of providing high-throughput 3D phase-contrast tomograms of each single cell is presented.
Identification of drug-resistant cancer cells in flow cytometry combining 3D holographic tomography with machine learning
Daniel Pirone,Lu Xin,Vittorio Bianco,Lisa Miccio,Wen Xiao,Leiping Che,Xiaoping Li,Pasquale Memmolo,Feng Pan,Pietro Ferraro +9 more
- 01 Nov 2022
TL;DR: In this paper , the authors used digital holographic flow cytometry to collect images of flowing cells and reconstructed their 3D tomographic phase and extracted meaningful morphometric features from the 3D and 2D phase maps through machine learning methods and finally compared their classification performance.
21
Beyond fluorescence: advances in computational label-free full specificity in 3D quantitative phase microscopy.
TL;DR: This paper presents a new computational method that promises to bridge the gap between QPI and FM for real-world applications by achieving label-free microscopic imaging at single-cell level to recognize and quantify different subcellular compartments.
15