Abstract
SIGNIFICANCE: Imaging flow cytometry allows highly informative multi-point cell analysis for biological assays and medical diagnosis. Rapid processing of the imaged cells during flow allows real-time classification and sorting of the cells. Off-axis holography enables imaging flow cytometry without chemical cell staining but requires digital processing to the optical path delay profile for each frame before the cells can be classified, which slows down the overall processing throughput. We present a method for real-time cell classification via label-free quantitative imaging flow cytometry using digital holography, offering a comprehensive representation of cellular structures, without the need for digital processing before automatic cell classification. AIM: We aim to develop an automatic cell classification scheme based directly on the off-axis holographic projections of the cells during flow and test it for stain-free imaging flow cytometry of white blood cells. APPROACH: After building a dedicated off-axis holographic microscopy system for acquiring white blood cells during flow, we apply deep-learning classification directly in the off-axis hologram space, rather than in the quantitative phase profile space. This way, we simplify computational processes and allow a significant increase in the cell classification throughput. In addition, by utilizing multiple-viewpoint holographic projections of the cells rotated during flow, instead of using a single projection, we obtain better classification results due to the additional cellular information gained. RESULTS: Our technique demonstrates increasing accuracy with additional viewpoint holographic projections from the optical system, achieving a 7.69% improvement when processing ten interferometric projections compared with a single interferometric projection (regular off-axis hologram). Our technique also outperforms using multiple optical path delay profile projections, requiring off-axis holographic digital preprocessing, by 17.95%, because the holographic projections are analyzed directly without preprocessing and includes the amplitude information as well. CONCLUSIONS: Our cell classification approach has great potential for high-throughput, high-content, label-free imaging flow cytometry for classification of large-scale cellular datasets and real-time cell classification during flow in clinical settings.