Summary
Organoids are microscopically small patient-derived 3D organs that can be cultivated in the laboratory over months mimicking human organs and their functions in vitro. These mini-organs have a human genetic background, maintain disease traits in vitro and are currently available for almost every human organ. Due to these advantages, organoid research and its commercial applications are rapidly evolving and increasingly used. Given the high variability of these complex 3D structures, classical cell culture-based image quantification tools techniques do not accurately capture organoids in microscope images. This has resulted in much image quantification at our laboratory - like in many others - being performed using manual time-consuming tools with a high researcher variability. In sum, there is a global challenge to in a standardized manner quantify organoid experiments. We propose the first Software as a Service (SaaS) toolbox explicitly tailored to organoid imaging, building upon powerful AI-based algorithms for cutting-edge image quantification and independent of the underlying microscope and culture system hardware. The SaaS seamlessly fits in the analysis workflow by offering a user-friendly approach to upload brightfield and immunofluorescence microscope images and videos of organoid cultures, which - at the core of our product - are automatically quantified by AI-models generating a range of organoid metrics. Overall, the provided automation drastically reduces analysis time, promotes accurate phenotypical organoid analyses and ensures standardization of results between different researchers, culture conditions, and imaging hardware. Integrated in an easy-to-use web app, the SaaS tool is of great interest and essential for researchers working in the emerging field of organoids, as shown by the high utilization in our laboratory and the high interest from external partner institutions.
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| Web resources: | https://cordis.europa.eu/project/id/101123031 |
| Start date: | 01-06-2023 |
| End date: | 30-11-2024 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
Organoids are microscopically small patient-derived 3D organs that can be cultivated in the laboratory over months mimicking human organs and their functions in vitro. These mini-organs have a human genetic background, maintain disease traits in vitro and are currently available for almost every human organ. Due to these advantages, organoid research and its commercial applications are rapidly evolving and increasingly used. Given the high variability of these complex 3D structures, classical cell culture-based image quantification tools techniques do not accurately capture organoids in microscope images. This has resulted in much image quantification at our laboratory - like in many others - being performed using manual time-consuming tools with a high researcher variability. In sum, there is a global challenge to in a standardized manner quantify organoid experiments. We propose the first Software as a Service (SaaS) toolbox explicitly tailored to organoid imaging, building upon powerful AI-based algorithms for cutting-edge image quantification and independent of the underlying microscope and culture system hardware. The SaaS seamlessly fits in the analysis workflow by offering a user-friendly approach to upload brightfield and immunofluorescence microscope images and videos of organoid cultures, which - at the core of our product - are automatically quantified by AI-models generating a range of organoid metrics. Overall, the provided automation drastically reduces analysis time, promotes accurate phenotypical organoid analyses and ensures standardization of results between different researchers, culture conditions, and imaging hardware. Integrated in an easy-to-use web app, the SaaS tool is of great interest and essential for researchers working in the emerging field of organoids, as shown by the high utilization in our laboratory and the high interest from external partner institutions.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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