Biomedical Image Informatics
We are investigating and developing methods providing efficient access to information encoded in biomedical images.
Imaging is one of the key data acquisition technologies in medicine and life sciences and an extremely fast evolving technology delivering increasingly detailed insights into structure and function of organisms. This rich source of information enables fundamental insights into biological processes, but also supports the ongoing efforts to realize personalized medicine. However, scientists and physicians are confronted with an ongoing explosion of imaging data that has to be processed, inspected, analyzed and correlated and the ability to efficiently get access to the core information encoded in this data decides on its final usefulness.
The Biomedical Visualization Group is an interdisciplinary team addressing the above challenge by providing novel visual computing solutions covering complete image based workflows for medicine and life science. Our R&D portfolio ranges from high performance image analysis solutions for radiology, to visualization solutions for intervention planning and the development of complete IT infrastructures for management, analysis, visualization and data mining of large image data collections for neurosciences.
- Biomedical image processing and analysis
- Machine learning
- Biomedical visualization
- Image data mining and spatial indexing
- Image data management
- Human computer interaction
Martin Trapp, Florian Schulze; Alexey A. Novikov; Laszlo Tirian; Barry J. Dickson; Katja Bühler
Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images. Neuroinformatics, 2016
Hladůvka, Jiří; Enkhbayar, Asura; Norman, Benjamin; Ljuhar, Richard
Automated ROI Placement and Trabecula-Driven Orientation for Radiographic Texture Analyses of Calcaneus, (to appear in) ISBI 2016
Swoboda, N, Moosburner, J, Bruckner, S, Yu, J, Dickson, B, and Bühler, K.
Visualization and Quantification for Interactive Analysis of Neural Connectivity in Drosophila. Computer Graphics Forum. (Accepted)
Ganglberger, F., Schulze, F., Tirian, L., Novikov, A., Bühler, K., & Langs, G.
Structure-Based Neuron Retrieval Across Drosophila Brains. Neuroinformatics, 2014
Major, D., Hladuvka, J., Schulze, F., & Bühler, K.
Automated Landmarking and Labeling of Fully and Partially Scanned Spinal Columns in CT Images. Medical Image Analysis, 17(8), pp. 1151–1163, 2013
eScience Software-Platform Brain*
Understanding how the brain works is one of the biggest challenges addressed by neuroscientists today. This research is highly data intensive and requires dedicated software infrastructures to enable and accelerate the discovery of the complex interplay of genes, structure and function. Brain* (see https://braingazer.org/) is a complete software suite supporting image based neural circuit researchat all stages. It provides a complete integrated software infrastructure supporting data management fromimage alignment (BrainWarp), storage (BrainBase), publication (BrainBaseSync), quality control and datavisualisation (BrainGazer) through to information retrieval and knowledge discovery in large and dynamicallygrowing data sets of confocal images and related annotated structures (BrainMining). The framework has beendesigned to handle extremely large amounts of image data in a stable manner and ensures fast semantic andspatial accessibility of all stored data. Brain* has been initially developed by VRVis in cooperation with theInstitute of Molecular Pathology in Vienna, where it provides the IT ecosystem for drosophila research.
Healthcare: AGFA Healthcare, BrainCon, Aquilab, Medtronics, Medical University Vienna, Universitaetsklinikum Freiburg, Oncolopole Toulouse, Donau University Krems, Faculdade de Medicina da Universidade de Lisboa, Medical University of Graz
Life Sciences and Biology: Institute of Molecular Pathology Vienna, CNRS - Tefor.net, HHMI, Baxter, University Konstanz, Correlated Multi Modal Imaging Node Austria, coopNatura
Computer Science and HCI: TU Wien, Vienna University, RWTH Aachen, University Magdeburg, TU Delft
Visual Computing Techniques for Automated Detection of Osteoporosis and Osteoarthritis
Knowledge Models for Robust Biomedical Image Analysis
strategic research project
Next Generation Workflows for Interactive Knowledge Generation from Images and Simulations.
Software for the Use of Multi-Modality images in External Radiotherapy
The analysis, visualisation and exploration of high dimensional image spaces are the subject of research of the KAFus project.