LIFE SCIENCES


‘An automated system could cut the time taken to find failures and reduce the labour-intensive steps in the lab’


What makes event-based sensing different


become mainstream. A few labs are leading the way but widespread adoption is probably several years away. As Rivers said: ‘We’ll see significant movement in the next five years and, in the labs that are already somewhat automated and are willing to invest, we’re going to see a massive transition in the next decade.’


Beyond pathology But as pathologists around the world get ready for the inevitable digital overhaul, many tech businesses are also developing new imaging-related devices and systems that promise to reduce analysis times in the lab. A case in point is technology consultancy, Cambridge Consultants, which has developed PureSentry, a contamination detection system for cell and gene therapy monitoring that uses an event-based sensor from French firm Prophesee. As Cambridge Consultants’ senior


physicist, Josh Gibson, pointed out, he and colleagues wanted to develop a system that would cut the time and costs of arduous sterility testing in this sector. Tis process firstly demands a ten-day culture period followed by the actual sterility test, which can also take up to another ten days. Gibson said: ‘In the gene therapy market,


each dose can cost around $500,000, and nearly one in 1,000 doses fail due to sterility testing, meaning seriously ill patients will not receive treatment. We realised that an automated system could cut the time taken to find failures – giving patients more opportunity to get their treatment – and reduce the labour-intensive steps in the lab.’ By chance, Gibson and colleagues came


across Prophesee’s Metavision event-based sensor around two years ago. Tey integrated


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is that an output is only generated when the change in contrast exceeds a threshold. In comparison, conventional cameras will sample every pixel at a fixed rate. Such an event-based approach reduces power, latency and data processing requirements compared to frame-based systems, while achieving much higher dynamic ranges. Events can be recorded that would require conventional cameras to operate at 10,000 images per second or more. And critically, this approach makes label-free imaging a reality, even for low contrast targets, such as cells – spelling good news for cell and microbe monitoring. ‘When we’re flowing our cell culture


medium past the microscope, we could be seeing only one bacteria among many millions of human T cells in our millilitre sample,’ said Gibson. ‘So what we really want to do is cut out all of the background and unnecessary data in a scene, and pick out what is important – event-based sensing is great for this as it only responds to fluctuations in brightness.’ As part of the PureSentry closed-loop


Viewing lung tissue using uPath software from Roche


it into an automated contamination detection system that operates in real-time, which ended up as PureSentry. Inspired by the human retina, Prophesee’s


event-based Metavision sensor comprises 300,000 independent and asynchronous pixels that are responsive to low contrast, transient events. Tese pixels are essentially relative-change detectors, and activate independently according to any change in contrast detected in a scene. When activated, the pixels create a stream of time-stamped events in which their location within the sensor and the direction of the change in brightness - event-on or event-off - are encoded. ‘We have software algorithms that mimic the way the brain leverages visual information... Each intelligent pixel is triggered by motion and decides when to activate,’ explained Guillaume Butin, marketing director at Prophesee. ‘One pixel activating is an event, and we only see what moves.’


system, an inverted microscope equipped with Prophesee’s event-based camera captures data on cells as these and associated media are continuously pumped from a bioreactor through a microfluidic cell. Cell data is then sent to post-processing software including an artificial intelligence- event classifier algorithm and decision algorithm to determine if the sample is sterile or contaminated. ‘Te event-based sensor simply slotted


into existing microscope setups – we could easily compare how this worked compared to standard cameras, and while doing this were able to develop our microfluidic rig,’ said Gibson. As the physicist pointed out, thanks to


the large dynamic range of the sensor, the PureSentry system can use an LED light source rather than laser light, cutting overall costs and reducing the risk of photodamage to cells. Te automation software for the system’s hardware was based on Prophesee’s Python APIs, while the Cambridge Consultants researchers developed post- processing software using machine learning recurrent neural network algorithms, which, g


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