Virtual tissue staining by deep learning

Introduction

Our goal is to create a virtual tissue-staining device to replace manual chemical-staining techniques by integrating optics and deep learning.

Importance of Chemical Staining

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.

Advancements with Deep Learning

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 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.

Proposed Solution

In order to make virtual tissue-staining more accessible to end-users, we propose a device based on a simple optical system with integrated deep-learning-powered virtual-staining software.

Market Opportunity

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.

Startup Launch

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 approximately 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.