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CyberScoPy

Financiado

Cerrado

Smart, Event-Based Microscopy for Cell Biology

Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes are passive systems and are still very limited in their operating capacity which limits in several ways our ability to... ver más

31/03/2025

CNRS

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-09-12

ERC-2023-POC: ERC PROOF OF CONCEPT GRANTS Objective:Objectives:The ERC Proof of Concept Grants aim at facilitating exploration of the commerci...

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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1 Participantes

CNRS

150.00K€ | Lider

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Duración del proyecto: 18 meses Fecha Inicio: 2023-09-12
Fecha Fin: 2025-03-31

Líder del proyecto

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes are passive systems and are still very limited in their operating capacity which limits in several ways our ability to identify and image complex biological events in real time and at the proper 4D scales. They could become much more powerful investigation systems for the life science if they were endowed with user-friendly, unsupervised decision-making algorithms, transforming microscopes into fully responsive and automated measurement devices. Indeed, we are at a moment when smart systems and artificial intelligence are being used everywhere, including in laboratories to improve the functioning of many (scientific), however outdated preprogrammed microscopy workflows are still being routinely implemented. The ability to employ real-time image analysis to inform, optimize and adjust the settings of ongoing image acquisitions would be a game changer for studying complex, dynamic cellular processes. To address this issue, we have developed a pilot software, CyberSco.Py, which enables the possibility to conduct image analysis in real time (using deep learning) to trigger modifications in the acquisition settings thus alleviating the need for manual input and supervision. This allows for the implementation of novel classes of experiments that cannot be achieved with current solutions. Within the context of this PoC, CyberSco.Py will be developed into a user-friendly software capable of smart automation of microscopy systems an their add-ons (e.g. microfluidics, temperature controls, etc...). As such, CyberSco.Py has the potential to revolutionize the power and scope of microscopy experiments for quantitative cell biology with broad implications for the microscopy sector.

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