Rare cell classification using label-free imaging flow cytometry via motion-sensitive-triggered interferometry
Year: 2025
Type: article
Abstract: a single raw-interferogram analysis, significantly reducing data volume. The raw interferometric data serves as an input to a deep neural network for rare-cell classification. We demonstrate the use of this system to detect and grade rare cancer cells in blood, where the event camera is used to rapidly classify between the common white blood cells and the rare cancer cells, and the interferometric camera is used to grade the cancer cell type (primary/metastatic), as a human model for detecting and grading circulating tumor cells in liquid biopsies. This hybrid approach enables efficient data acquisition, rapid processing, and high sensitivity, significantly reducing computational load, and is expected to find various applications in detecting and processing rare cells in imaging flow cytometry.
Source: Lab on a Chip
DOI: 10.1039/d5lc00634a
Institution: Tel Aviv University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414442568