TissueGnostics’ industry session at IUIS 2025 was a success, uniting immunologists around advances in spatial biology and AI-powered imaging. Thanks to Dr. Ecker, A/Prof. Mechtcheriakova, Prof. Batra, and all who joined us. Until next time!

AI-powered tissue classification applies machine learning to identify and categorize tissue types. It supports consistent, quantitative analysis of morphology, tumor microenvironments, and disease processes in research and diagnostics.

This year TissueGnostics launches a new scanning platform for the acquisition, management, visualization and analysis of imaging data – Colubris. This boxed solution can help you dynamically optimize your workflows with on-the-fly, AI-assisted image analysis and data mining, even during sample scanning.

Through the power of spatial phenotyping, we gain the ability to shine a spotlight on different cell types and their environments.

The StrataQuest Organoid App provides reliable and efficient high-throughput validation and quantification for organoids co-cultured alongside immune cells.

AI is transforming imaging cytometry by enabling automated, accurate nuclei segmentation and tissue detection. With solutions like StrataQuest, researchers gain higher precision, efficiency, and insights into cellular structures for biomedical research and diagnostics

Immunofluorescence image analysis combined with tissue cytometry enables in-depth study of protein expression, cell interactions, and spatial biology. Learn how multiplex IF, whole-slide imaging, and AI-driven analysis are shaping the future of digital pathology.

Dr. Amirreza Mahbod presents how deep learning enhances nuclei segmentation and tackles challenges in histological image analysis.

QUAREP-LiMi releases standardized guidelines for light microscopy. Dr. Rupert Ecker contributed to checklists improving image quality, reproducibility, and communication.

Deep learning has become a powerful tool in biomedical imaging, enabling accurate cell segmentation, phenotype detection, and analysis of complex tissue structures. TissueGnostics offers AI-driven nuclei segmentation to improve efficiency and precision in image analysis.

Zhou et al. applied AI-assisted analysis to pancreatic adenocarcinoma, quantifying CD8+ TILs, cancer stem cells, and tumor budding. StrataQuest revealed immune and tumor features predictive of patient survival.

Accurate cell segmentation is the foundation of tissue cytometry. AI-powered methods, such as deep learning, improve accuracy, reduce errors, and streamline workflows. TissueGnostics integrates AI-based segmentation with phenotyping, spatial analysis, and dot detection.

Deep learning is transforming cell counting by enabling precise nuclei segmentation, even in dense tissue microenvironments. Learn how TissueGnostics applies deep neural networks (DNNs) to automate and improve accuracy in tissue cytometry workflows.

Cell counting is central to biomedical research, but accuracy depends on advanced algorithms. This blog explores manual vs automated methods, and how TissueGnostics’ AI-driven cell counting algorithms enable reliable, high-throughput image-based analysis.

TG and the University Clinic of Dentistry (Danube Private University) have received FFG funding for AutOPathStage. This project aims to develop a deep learning–based decision support system for classification of oral dysplasia – a disease of the oral cavity with malignant potential.

Dr. Rupert Ecker, CEO of TissueGnostics, gives a short introduction of TissueGnostics’ tissue cytometry solutions with the main focus on immunophenotyping and spatial analysis of the tumor microenvironment in situ.

In Trillium Diagnostik, Dr. Felicitas Mungenast and Alexander Stickler-Barang highlight the role of immunophenotyping in tissue sections for diagnosing diseases. The full article is available in German.

Next-generation digital pathology combines whole-slide imaging with AI-driven analysis to transform cancer research and diagnostics. Learn how TissueGnostics supports this shift with advanced imaging and automated decision-support tools.