Eden Dotan, Dana Yagoda-Aharoni, Eli Shapira, Natan T. Shaked
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引用次数: 0
Abstract
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 via a single raw-interferogram analysis, significantly reduicng data volume. The raw interferometric data serves as an input to a deep neural network for rare-cell classification. We demonstrate using 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.
期刊介绍:
Lab on a Chip is the premiere journal that publishes cutting-edge research in the field of miniaturization. By their very nature, microfluidic/nanofluidic/miniaturized systems are at the intersection of disciplines, spanning fundamental research to high-end application, which is reflected by the broad readership of the journal. Lab on a Chip publishes two types of papers on original research: full-length research papers and communications. Papers should demonstrate innovations, which can come from technical advancements or applications addressing pressing needs in globally important areas. The journal also publishes Comments, Reviews, and Perspectives.