MICROSCOPY & IMAGING
as tissue cytometry to determine the molecular processes that trigger diseases or lead to the progression of a disease in a specifi c patient, thereby providing valuable information for diagnosis, prognosis and adequate treatment strategies. Nowadays, for an individual patient, entire panels of biomarkers/ cellular phenotypes as well as their spatial relationships must be assessed, whereas in classical histopathology a patient’s diagnosis depends on the visual examination of an H&E stained tissue slide. On one hand, our ongoing discoveries of novel biomarkers lead us to a potentially higher diagnostic precision and more optimised therapies for patients, and on the other hand, we have to face the challenge of increasing our analytical and diagnostic capacity. T e way forward is to deliver innovations in the form of automated quantifi cation and interpretation through computer-assisted analysis and decision support systems.
HOW CAN AI ENHANCE TISSUE CYTOMETRY? Artifi cial intelligence (AI) in the context of tissue cytometry can be characterised as machine learning/deep learning models, i.e. algorithms specialising in pattern recognition applied to microscopic images of histological samples. Over the past decades these tools have continuously evolved to become more robust, requiring minimal user input for the automated detection and classifi cation of complex tissue structures/entities. AI tools such as machine learning-based tissue classifi ers (i.e. automated recognition of histological structures) and deep learning neural network-based nuclei/single cell segmentation, can be leveraged to advance the speed and accuracy of tissue
AI tools can be used to defi ne spatial context of cellular phenotypes within the tumour microenvironment
A sophisticated and automated skin analysis was made possible by AI tools in StrataQuest, using deep learning based nuclei segmentation and machine learning tissue classifi cation
cytometry. Combining these AI tools can make what was once considered an analysis too complex for the human mind attainable. T e ability to rely on an autonomous
and robust deep learning-based algorithm for nuclei segmentation especially suited for challenging tissue environments (high cellular density, weak staining intensity) can be seen as the basis of a successful tissue analysis. An accurate nuclei segmentation is used as a starting point for building more sophisticated tissue analyses (high-content phenotyping, spatial dynamics, etc.). For higher degrees of automation, machine learning-based tissue classifi ers focus on the segmentation of morphological substructures within a tissue section. A model is created by marking just a few areas representative of the specifi c morphological entities of interest (tumour area, blood vessels, immune cell clusters, etc.) directly on the digitised slide. T is model will be specialised for separating the tissue into specifi c classes and will automatically generate a binary mask for further measurements. To further analyse spatial interdependencies, identifi ed cellular phenotypes can be located in reference to detected tissue classes and other cellular phenotypes.
HOW CAN TISSUE CYTOMETRY ENRICH YOUR RESEARCH? TissueGnostics combines a high level of automation and expertise in both imaging and analysis and is thereby able
to provide a holistic tissue cytometry solution that covers everything from whole-slide acquisition to high-content image analysis and multidimensional data mining. Imaging modalities off ered by the company are multi-faceted, including high-throughput whole-slide scanning in brightfi eld, fl uorescence, multispectral, multiplexing and confocal modes. Taking advantage of the recent
advancements being made in AI, a machine learning-based tissue classifi er and a deep learning-based nuclei segmentation algorithm are integrated into TissueGnostics’ contextual tissue analysis software, StrataQuest, with the goal of making sophisticated tissue analysis more accessible. As AI continues to progress, the company is actively involved in shaping the future of digital medicine by participating in multinational research projects, focusing on the development of new machine learning algorithms (such as the HELICAL project). Yet, the company not only develops novel algorithms based on existing AI technologies; it also actively contributes to the development of novel AI technologies that will constitute disruptive, GDPR- and IVD-compliant intelligent products/tools for future precision medicine, such as the s3ai project.
Alex Barang & Felicitas Mungenast are with TissueGnostics.
www.tissuegnostics.com
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