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Virtual tissue staining by deep learning

Our goal is to create a virtual tissue-staining device to replace manual chemical-staining techniques by integrating optics and deep learning. Chemical staining of cell components plays an essential role in biomedical and pharmac... ver más

31/10/2024

UGOT - GOETEBORGS...

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

GOETEBORGS UNIVERSITET 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 2024-10-31

ERC-2022-POC2: ERC PROOF OF CONCEPT GRANTS2 Objective:Objectives:

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UGOT - GOETEBORGS UNIVERSITE...

150.00K€ | Lider

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Duración del proyecto: 18 meses Fecha Inicio: 2023-04-24
Fecha Fin: 2024-10-31

Líder del proyecto

GOETEBORGS UNIVERSITET 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 Our goal is to create a virtual tissue-staining device to replace manual chemical-staining techniques by integrating optics and deep learning. Chemical staining of cell components plays an essential role in biomedical and pharmaceutical research and practice. Cell structures of interest are highlighted using various chemical stains and imaged with the appropriate optical setup. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time consuming, labor intensive, and expensive. Recently, the use of deep learning has been proposed as a way to create images of virtually stained cell structures, thus mitigating the inherent problems associated with the conventional chemical staining. However, these methods are usually specialized for a specific application and, thus, highly dependent on the setting of the optical device used for acquiring the training data. In order to make virtual tissue-staining more accessible to end-users, we propose a device based on a simple optical system with an integrated deep-learning-powered virtual-staining software. This is an ideal moment to enter the virtual tissue-staining market because we can gain a first mover advantage. Further, we can take advantage of the fact that the tissue-staining market is expected to grow with a compound annual rate of roughly 8.5% until 2025 (up to 3400M USD), and to continue to grow for the foreseeable future. As part of this project, we aim to launch the startup company IFLAI to commercially exploit our virtual-staining technology and the prototype we will develop. With the startup IFLAI, we aim to provide ~20 jobs to university-educated individuals in the EU within the next 5 years. IFLAI has already received initial funding and support from two different organisations that support and believe in its venture.

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