Neuromorphic imaging cytometry: event-driven cell analytics powered by AI

Abstract

Imaging flow cytometry (IFC) provides spatial resolution and multi-parametric measurements to characterise cells of interest. It enables profound insights into cell signalling, co-localisation, cell-to-cell interaction, and DNA studies. However, the use of traditional frame-based sensors (FBS) introduces the classic trade-off between speed, resolution, and sensitivity, where improving one often degrades the others. This limitation has hindered both technological progress and widespread adoption. The large data volumes generated by IFC further complicate real-time applications. The spatial richness of IFC data, however, makes it highly compatible with machine learning (ML), enabling automated analysis, gating, and the discovery of rare events. To overcome FBS constraints, neuromorphic vision sensors (NVS) have recently been introduced. Their event-driven design, characterised by data sparsity, provides greater data-processing efficiency, temporal resolution, and fluorescence sensitivity compared to conventional imaging methods. This review examines advances in flow cytometry and IFC, evaluating their respective strengths and drawbacks. It highlights innovations in NVS that address long-standing limitations, explores recent advances in neuromorphic imaging cytometry (NIC) across different modalities and ML approaches, and discusses current challenges and future directions shaping this emerging field. NIC represents a pivotal step toward real-time, data-efficient cytometry platforms that integrate event-driven sensing with spiking neural computation.