Computational cytometer based on magnetically modulated coherent imaging and deep learning

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications. Rare cells of medical significance can be detected in blood by a high-throughput imaging technique that analyzes movements of magnetically-labelled target cells using deep-learning. Analysis of magnetically-modulated light interference using an artificial neural network enhances the ability of the system to specifically and sensitively detect the tagged cells as they respond to a periodically changing magnetic field. Researchers in the USA, led by Aydogan Ozcan at the University of California, Los Angeles, demonstrated the technique using rare cancer cells added to blood. They achieved a detection limit as low as ten cells per milliliter. The optical system uses a holographic process called speckle imaging, which is much more compact and achieves higher throughput than traditional methods. It holds significant potential for the rapid analysis of blood and other bodily fluids to diagnose and monitor disease.