Rare cell classification using label-free imaging flow cytometry <i>via</i> motion-sensitive-triggered interferometry

We present a label-free imaging flow cytometry system that integrates a microfluidic chip, imaged by a motion-sensitive (event-based) camera and an interferometric phase microscopy module using a simple frame-based camera. The event camera captures activity from the flowing cells, corresponding to thousands of frames per second, and triggers the significantly slower interferometric camera when a rare cell, requiring more sensitive analysis, is detected <i>via</i> 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.