Researchers have developed a novel imaging flow cytometry system that combines cutting-edge neuromorphic cameras and photonic neuromorphic processors to rapidly classify different types of cells. This breakthrough technology could significantly improve disease diagnostics, drug discovery, and personalized medicine. By leveraging neuromorphic engineering and photonic integrated circuits, the system achieves high classification accuracy with a massive reduction in computational complexity, paving the way for portable, low-power, and real-time cell analysis.
The Need for Faster and More Efficient Cell Imaging
Imaging flow cytometry (IFC) is a powerful technique that combines the high-throughput capabilities of traditional flow cytometry with detailed imaging of individual cells. This allows researchers to extract rich morphological and spatial information about cell populations, which is crucial for applications like disease diagnosis, drug discovery, and personalized medicine. However, conventional IFC systems often struggle to balance high-speed imaging with the preservation of spatial details, leading to motion blur and data-intensive outputs.
Combining Neuromorphic Sensing and Photonic Processing
To address these challenges, the researchers in this study leveraged a unique combination of neuromorphic sensing and photonic neuromorphic processing. They used a methacrylate’>PMMA (polymethyl methacrylate) beads flowing through the microfluidic channel. Remarkably, this performance was achieved with a significant reduction in the number of trainable parameters in the digital neural network, by a factor of up to 22 compared to a standalone digital classifier.
Toward Portable and Efficient Cell Analysis
The combination of neuromorphic sensing and photonic neuromorphic computing offers a promising path forward for the development of portable, low-power, and real-time IFC systems. By offloading computationally intensive tasks to the analog photonic domain, the digital backend can be dramatically simplified, leading to improved energy efficiency and reduced latency. This breakthrough paves the way for advanced cell analysis that can be easily deployed in clinical settings, drug development laboratories, and even in-home diagnostics, transforming the future of personalized healthcare.
Author credit: This article is based on research by I. Tsilikas, A. Tsirigotis, G. Sarantoglou, S. Deligiannidis, A. Bogris, C. Posch, G. Van den Branden, C. Mesaritakis.
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![methacrylate’>PMMA](https://en.wikipedia.org/wiki/Event-based<em>camera’>neuromorphic camera</a> that captures contrast changes in the scene, rather than traditional frame-based imaging. This event-based approach allows the camera to achieve <b>ultra-high frame rates of up to 1 billion events per second</b>, while drastically reducing the amount of data generated.</p>
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<p>To handle the high-speed, sparse data from the neuromorphic camera, the team developed a photonic neuromorphic processor based on <b>optical spectrum slicing (OSS)</b>. This analog optical circuit performs a convolution-like operation on the input data, extracting relevant features without the need for computationally intensive digital pre-processing. The pre-processed data is then fed into a lightweight digital neural network for final classification.</p>
<p><h3>Achieving High Accuracy with Reduced Computational Complexity</h3>
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<p>The experimental results demonstrated that the combined neuromorphic sensing and photonic neuromorphic processing approach achieved a classification accuracy of <b>98.6%</b> for discriminating between different-sized <a href=){kind=link}