Image Analysis

In the Image Analysis research area, we develop and apply methods for computational processing and analysis of digital microscopy images, especially for solving problems in biology and medicine. A wide range of biomedical research projects rely on microscopy data which require specifically designed image analysis workflows to support the identification of complex biological processes. In close collaboration with our partners from research and industry, we develop algorithms and software that analyze microscopy images, for example fluorescence microscopy images of cells or other kinds of organic material. Our focus in algorithm development is mainly on methods of signal processing, machine learning, and computer vision for information extraction and interpretation. Besides filtering and denoising for pre-processing and feature extraction methods, we use machine learning approaches such as deep learning for object detection, segmentation, and object tracking. In addition to workflows and algorithms, we implement image analysis software (e.g. Spotty) for biological research to automatically detect, track, and analyze cells and particles to support fundamental research and drug development. For image analysis, we build on tools and frameworks such as MATLAB, OpenCV, and TensorFlow.



Selected Publications

2021 Jonas Schurr, Christoph Eilenberger, Peter Ertl, Josef Scharinger, and Stephan M. Winkler: Automated Evaluation of Cell Viability in Microfluidic Spheroid Arrays Proceedings of the 10th International Workshop on Innovative Simulation for Healthcare, 18th International Multidisciplinary Modeling & Simulation Multiconference 2021
2016 Daniela Borgmann, Sandra Mayr, Helene Polin, Susanne Schaller, Viktoria Dorfer, Christian Gabriel, Stephan Winkler, and Jaroslaw Jacak: Single Molecule Florescence Microscopy and Machine Learning for Rhesus D Antigen Classification Scientific Reports 6

Selected Projects


Transfusion of platelets is used either prophylactic to reduce the risk of clinical threatening bleedings or therapeutic to treat acute life-threatening thrombocytopathic-relevant bleedings (e.g. thrombocytopenic traumatic injuries, hematopoietic stem cell transplantation, chemotherapy). Activation and enhancement of platelet reactivity is the key step during hemostasis. After the first activation at the point of blood vessel trauma the platelets then aggregate and form a stable thrombus. This process is irreversible; the platelets are removed by macrophages during the subsequent following tissue regeneration.


In general, platelet concentrates are stored at +22°C ± 2° C in gas-permeable, sterile plastic bags; the maximum shelf life is 5 days. Anticoagulants (Heparin, Citrate) are used to prevent activation and aggregation of platelets before transfusion. Lethal sepsis in the course of platelet transfusion due to bacterial contamination can occur especially when using stored platelets at the end of their shelf life.


Aim of this project is to extend the shelf life of platelet concentrates and to characterize their actual state via real-time analysis. Utilizing high-resolution microscopy techniques on different biochemical surfaces allows monitoring activation-induced changes from the cellular - down to the molecular - level. Physico-chemical parameters during storage will be characterized. Moreover, the project is aimed to classify the interaction reactivity of (transfused) platelets following storage with the cells of the individual receiver.


FH-Prof. DI Dr. Birgit Plochberger


Daniela Borgmann MSc.

Michael Brodesser

DI Dr. Jaroslaw Jacak

Sandra Mayr MSc.

Prof.(FH) DI Dr. Stephan Winkler


10/2015 - present

Research Areas



Research Institutions

University of Applied Sciences, Upper Austria, Hagenberg Campus

University of Applied Sciences, Upper Austria, Linz Campus

Blutzentrale Linz, LBI Trauma Care Consult

Catalysts Linz

Sponsored by Innovative Upper Austria 2020

Research focus

Software technology and application 
Medical engineering

Back to Projects