Since VRVis was established, we have been developing machine learning and deep learning methods to support radiologists in many diagnostic tasks in routine clinical practice. VRVis solutions in radiology have been published in high-impact journals and have been patented as well as being successfully integrated into our customers’ products. They are already accelerating radiology workflows in hospitals worldwide.
Virtual training, intervention and planning tools have long been an important work principle in surgery. In close cooperation with surgeons, VRVis has created both preoperative planning tools and solutions for intraoperative support for interventions, for example in neurosurgery, orthopaedics and cardiac surgery. Depending on the application, we combine our extensive expertise in real-time visualisation of 3D image data, automatic image analysis, human-computer interaction, and AR and VR experience in tailor-made solutions.
Many of our solutions have been used in clinical situations, won several awards and been published in high-impact journals.
F. Schulze, K. Bühler, A. Neubauer, A. Kanitsar, L. Holton, and S. Wolfsberger, "Intra-operative virtual endoscopy for image guided endonasal transsphenoidal pituitary surgery," Int J Comput Assist Radiol Surg, vol. 5, no. 2, pp. 143–154, 2010.
J. Beyer, S. Wolfsberger, and K. Bühler, "High-Quality Multimodal Volume Rendering for Preoperative Planning of Neurosurgical Interventions," IEEE Trans. Visual Comput. Graphics, vol. 13, no. 6, Nov. 2007.
S. Wolfsberger, A. Neubauer, K. Bühler, R. Wegenkittl, T. Czech, S. Gentzsch, H.-G. Boecher-Schwarz, and E. Knosp, "Advanced virtual endoscopy for endoscopic transsphenoidal pituitary surgery," Neurosurgery, vol. 59, no. 5, pp. 1001–1009, Nov. 2006.
A. Neubauer, S. Wolfsberger, M.-T. Forster, L. Mroz, R. Wegenkittl, and K. Bühler, "STEPS - An Application for Simulation of Transsphenoidal Endonasal Pituitary Surgery," IEEE Visualization, pp. 513–520, 2004.
A. Neubauer, K. Bühler, R. Wegenkittl, A. Rauchberger, and M. Rieger, "Advanced virtual corrective osteotomy," International Congress Series, vol. 1281, pp. 684–689, 2005.
Analysing large volumes of data and selected patient cohorts has long been one of the main approaches in research, for example to investigate basic epidemiological processes, and develop personalised prediction models and treatment strategies for patients. In researching rare diseases, when patient numbers are insufficient, it is less the volume and more the combination of complex heterogeneous data sets that plays a role in recording as complete an image as possible of the disease and its molecular, demographic and clinical background. VRVis has already implemented several projects which support medical professionals and life scientists in, for example, research into rare types of cancer and in stable statistical analysis of patient cohorts.
Successful radiotherapy and precise radiation planning are closely linked. At the heart of both is patient safety, which is ensured by the most exact targeting of the tumour so as to prevent unnecessary radiation and damage to healthy tissue. Within the framework of the interdisciplinary EU project SUMMER, new approaches to data visualisation have been developed by VRVis. Through the combined visualisation of different imaging methods and the possibility of interacting with the data, the medical professionals from oncology, radiology and physics are able to better identify the biological target volume of a tumour, therefore allowing them to plan more tailored radiotherapy. SUMMER was awarded a European prize.
M. Schlachter, R. G. Raidou, L. P. Muren, B. Preim, P. M. Putora, and K. Bühler, "State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning," Computer Graphics Forum, vol. 38, no. 3, pp. 753–779, Jun. 2019.
M. Schlachter, T. Fechter, S. Adebahr, T. Schimek-Jasch, U. Nestle, and K. Bühler, "Visualization of 4D multimodal imaging data and its applications in radiotherapy planning," J Appl Clin Med Phys, vol. 33, pp. 136–11, Oct. 2017.
M. Schlachter, T. Fechter, M. Jurisic, T. Schimek-Jasch, O. Oehlke, S. Adebahr, W. Birkfellner, U. Nestle, and K. Bühler, "Visualization of Deformable Image Registration Quality Using Local Image Dissimilarity," IEEE Trans. Med. Imaging, vol. 35, no. 10, pp. 2319–2328, 2016.
M. Nunes, B. Rowland, M. Schlachter, S. Ken, K. Matkovic, A. Laprie, and K. Bühler, "An integrated visual analysis system for fusing MR spectroscopy and multi-modal radiology imaging," IEEE VAST, pp. 53–62, 2014.
Training AI algorithms requires a great amount of data. However, raw data often contains sensitive information. Homomorphic encryption offers a solution for secure machine learning - with protected sensitive data.
The aim of the application project IVC Multi is to research novel intelligent visual computing methods supporting decision-making in automotive industry, medicine, and life sciences based on ensembles of heterogeneous, multi-scale and/or multi-temporal data.
The aim of the project IVC Stream is to research novel visual computing solutions for simulation and measurement data.
This project aims at accelerating and automating image-based decision making with an application focus on medicine, recycling and quality assurance processes in manufacturing.
An augmented reality solution for optimizing process development in laboratories and monitoring ongoing experiments supports pharmaceutical research.
VRVis a founding member of the Austrian BioImaging/CMI, which is a professional consortium of multiple Austrian science institutions and the official Austrian Euro-BioImaging initiative.
COMULIS is an EU-funded COST Action that aims at fueling collaborations in the field of correlated multimodal imaging (CMI).
The long-term vision of this applied research project is to use available data resources to improve image-based diagnostics based on complex data in daily clinical routine.
The combination of "liquid biopsies", machine learning and data visualization aims to enable earlier and more accurate prediction of a relapse in children with cancer.
The strategic project forms the organizational and scientific hub for the realization of an area wide integrative visual computing approach. It covers joint strategic research and development on fundamental challenges in all application projects.
Visual computing for medicine: image processing solutions for new applications in radiology.
DEXHELPP develops new methods to support analysis, planning and control in health care by combining decision analysis, data security, data management, statistics, mathematical modelling, simulation and visual analysis.
Visual computing techniques for the automated detection of osteoporosis and osteoarthritis.
Software for the use of multi-modality images in external radiotherapy.
The analysis, visualization and exploration of high-dimensional image spaces are the subject of the KAFus project.
On March 3, 2020, Katja Bühler, head of our Biomedical Image Informatics Group, was awarded with the renowned TU Women's Prize.
Paper "ICthroughVR: Illuminating Cataracts through Virtual Reality" by Katharina Krösl is nominated for the Best Conference Paper at the IEEE Virtual Reality conference, which takes place from 23 to 27 March 2019 in Osaka (Japan)!
Visual computing for computer-aided diagnostics and operation planning.
A. Neubauer, M. T. Forster, L. Mroz, R. Wegenkittl, K. Bühler, STEPS - An Application for Simulation of Transsphenoidal Endonasal Pituitary Surgery, in Proceedings of IEEE Visualization 2004, pp 513-520. 2004, IEEE Vis 2004 Best Applications Paper
The European project SUMMER won the award 'Les étoiles de l'Europe'.
Sebastian Zambal, Jiří Hladůvka, Armin Kanitsar, Katja Bühler, Shape and Appearance Models for Automatic Coronary Artery Tracking, WON MICCAI 2008 Contest: 3D Segmentation in Clinic: A Grand Challange.
J. Beyer, M. Hadwiger, S. Wolfsberger), K. Bühler, High-Quality Multimodal Volume Rendering for Preoperative Planning of Neurosurgical Interventions, in IEEE Transactions on Visualization and Computer Graphics 13(6) pp.1696-1703 / Proceedings of IEEE Vis 2007, Vis 2007 Best Application Paper
A. Neubauer, Endoscopy for Preoperative Planning and Training of Endonasal Transsphenoidal Pituitary Surgery.
C. Langer, M. Hadwiger, K. Bühler, Interaktive diffusionsbasierte Segmentierung von Volumendaten auf Grafikhardware, Bildverarbeitung für die Medizin 2005; GI Informatik Aktuell; Springer Verlag. pp 168-17, BVM 2005 Best Poster
S. Wolfsberger, M. Donat, A. Neubauer, K. Bühler, T. Czech, E. Knosp, Virtuelle Endoskopie in der transsphenoidalen Hypophysenchirurgie, CURAC 2005 Best Poster
M. Meissner, B. Lorensen, K. Zuiderveld, V. Simha, R. Wegenkittl, Volume Rendering in Medical Applications: We've Got Pretty Images, What's Left to Do?, in IEEE Visualization 2002