Research project

Deep-Class-CTCs - Deep-learning classification of dynamically flowing circulating tumors cells imaged by quantitative phase microscopy (DFM.AD001.403)

Thematic area

Physical sciences and technologies of matter

Project area

Sensori multifunzionali e dispositivi elettronici (DFM.AD001)

Structure responsible for the research project

Istituto di cibernetica "Edoardo Caianiello" (ISASI)

Project manager

PIETRO FERRARO
Phone number: 0818675041
Email: pietro.ferraro@cnr.it

Abstract

The goal of this project is to develop dynamic deep-learning classifiers that can detect, analyze and monitor cancer cells in liquid blood samples. The blood samples will flow in a specialized micro-fluidic device that can rotate the cells during flow and provide an access to their perspective projections. The cells will be acquired by clinically-enabled interferometric imaging modules, developed together by both groups, providing the possibility to record the quantitative phase map projections of the blood and cancer cells in the full framerate of the camera used. Each of these topographic maps provides a great imaging contrast without cell staining, and also unique parameters that have not been attainable in previous attempts of imaging circulating tumor cells during flow, such as the cell volume, dry mass, and three-dimensional texture. For building the unified dataset of cancer cell maps, the optics-microfluidics system implementation and cell acquisition will be carried out in parallel in Italy and in Israel, where both groups have access to different types of cancer cells.

Start date of activity

02/12/2021

Keywords

Digital holographic microscopy, Quantitative phase imaging, Biological cell imaging

Last update: 29/03/2024