Summary
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everywhere, including in laboratories to improve the functioning of many (scientific), however outdated preprogrammed microscopy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.
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everywhere, including in laboratories to improve the functioning of many (scientific), however outdated preprogrammed microscopy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.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101138132 |
| Start date: | 01-10-2023 |
| End date: | 31-03-2025 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes are passive systemsand 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.
Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
Images
No images available.
Geographical location(s)
Search
