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


Tennis sport has gained major attraction during the last years. However, except for professional teams hardly any automatic player and play assessment, such as ball speed or stroke quality analysis is available.

Within the b-tastic project researchers at the Bioinformatics Research Group develop methods to automatically detect players, balls and strokes in tennis games using artificial intelligence and machine learning. These results are then further used by the project partner b-tastic sports GmbH to calculate quality assessments of the game.


The so developed system shall be available for hobby players and their clubs, enabling players to compare their performance to other players.


DI(FH) Dr. Viktoria Dorfer MSc.


Tobias Baumgartner BSc.

Andreas Haghofer MSc.

Theresa Hirz MSc.

Hannah Janout MSc.

Jacob PĆ¼hringer BSc. 

Jonas Schurr MSc.

Prof.(FH) DI Dr. Stephan Winkler




2020 - present

Research Areas


Project Partner

b-tastic sports GmbH


FFG - Basic Research Project

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