Neuromorphic imaging cytometry on human blood cells

Abstract Imaging flow cytometry (IFC) is a powerful cell analytic tool that exploits multi-parameters in single-cell images to characterise cell phenotypes and fluorescence information. It enables in-depth analysis of cell signalling, DNA repair and marker localisation. However, conventional frame-based acquisition is bound to the triangle of imaging constraints—speed, resolution and sensitivity, which has become an everlasting challenge to overcome during development. Neuromorphic photosensors detect contrast changes in a scene via individual-firing pixels, characterising superior data efficiency, temporal resolution and fluorescence sensitivity. In this work, we have developed a neuromorphic imaging cytometer (NIC) to capture fast-moving cell events, curating the first neuromorphic cell dataset with human blood cells, endothelial cells and artificial particles. Recently, this sensor has been adopted to address the limitations in IFC with prominent results in diverse modalities and machine learning approaches. Such a dataset serves as a baseline of healthy cell groups for both diagnostic and research purposes. In addition, the rich spatial information derived from cell images has exceptional uses with deep learning (DL) approaches to automate cell analysis, classification, sorting and gating strategy. We also trained a lightweight model combining the convolutional block attention module with a spiking neural network (CBAM-SNN) to automate cell analysis and classification. The proposed architecture has achieved a promising performance of 97% accuracy and F1 score with a significant reduction in computation requirements. Combining the data sparsity in neuromorphic imaging with a lightweight DL model and operation platform can enable next-generation, AI-driven cytometry to deliver point-of-care diagnostic and research solutions.