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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEEE Journal of Selected Topics in Quantum Electronics
Volume27
Issue number5
DOIs
Publication statusPublished - 2021

Keywords

  • Artificial intelligence
  • holography
  • machine learning
  • microfluidics
  • multilayer perceptrons
  • supervised learning

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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