Our portfolio

• Explainable decisions

• Analysis of mammography data

• Segmentation of soft tissue tumors in multimodal data

• Tuberculosis detection by AI-supported screening

• Segmentation and annotation of blood vessel

• General approaches to feature detection and segmentation of organs

• Fully automated semantic annotation of the spinal column

• Cardiac analysis

• Several patents on AI

Explainable decisions

How a machine came to a deep learning-based result from a radiology image classified into categories such as “diseased” or “healthy” is not initially comprehensible to the user. Neuronal networks behave like a black box - the learned features of an image that lead to a decision in the network cannot be interpreted by humans.

VRVis has developed methods that make the decision-making basis of a neuronal network visible to the user. These methods not only give medical professionals additional feedback on the reliability of the decision but also help developers to verify the quality of the trained network. 

Publication

• M. Wimmer, G. Sluiter, D. Major, D. Lenis, A. Berg, T. Neubauer, K. Bühler (2021): Multi-task fusion for improving mammography screening data classification. IEEE Transactions on Medical Imaging.

• D. Major, D. Lenis, M. Wimmer, G. Sluiter, A. Berg, and K. Bühler, "Interpreting medical image classifiers by optimization based counterfactual impact analysis," IEEE International Symposium on Biomedical Imaging, Iowa City, 2020.

• D. Lenis, D. Major, M. Wimmer, A. Berg, G. Sluiter, K. Bühler (2020): "Domain aware medical image classifier interpretation by counterfactual impact analysis," International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020.

Videos

• Explainable AI: Interpreting Mammography Image Classifiers
• Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis
• Domain Aware Medical Image Classifier Interpretation by Counterfactual Impact Analysis

Media report

• "XAI – Vertrauenswürdige Künstliche Intelligenz: Das Denken der Neuronalen Netze verstehen lernen", in: APA Science (11/2022)

Analysis of mammography data

Breast cancer is the most common type of cancer in women worldwide. Thus, the exact analysis of mammography images is an important task in radiology, since the early detection of risk factors and possible suspicious regions in the breast is crucial for successful therapy.

In cooperation with our long-term partner Agfa HealthCare, we developed various deep learning approaches for the analysis of mammography data. We combine several deep learning models that are focused to specific medical questions, such as where pathological lesions and microcalcifications are located in an image or whether a patient has denser breast tissue. This allows us to improve patient-level predictions based on the specific results of the individual models. The modular design of our solution and the possibility to access task specific decisions individually is increasing the explainability of the model and trust in its overall decision. To further increase trust in individual black box deep learning models and their decisions, we developed several novel approaches for explaining AI decisions in the context of image-based decision making.

Publication

• M. Wimmer, G. Sluiter, D. Major, D. Lenis, A. Berg, T. Neubauer, K. Bühler (2021): Multi-task fusion for improving mammography screening data classification. IEEE Transactions on Medical Imaging.

Videos

• Explainable AI: Interpreting Mammography Image Classifiers

Segmentation of soft tissue tumors in multimodal data

The exact segmentation of soft tissue tumors in multimodal data is of high importance in various medical domains, e.g. for surgery and biopsy planning or in radiotherapy. For this purpose multimodal data like MRI and PET/CT data is acquired, whereby the tumor segmentation may look different, depending on the modality and clinical task

In cooperation with the Medical University of Vienna, we developed and compared different methods to segment soft tissue tumors. We focused specifically on the modality- and task-specific soft tissue tumor segmentation, and how we can fuse multimodal data efficiently to improve the quality of the segmentation.

Publications

• T. Neubauer, M. Wimmer, A. Berg, D. Major, D. Lenis, T. Beyer, J. Saponjski, K. Bühler, "Soft Tissue Sarcoma Co-segmentation in Combined MRI and PET/CT Data", Multimodal Learning for Clinical Decision Support Workshop at MICCAI, 2020

• T. Neubauer, "Volumetric Tumor Segmentation on Multimodal Medical Images using Deep Learning" Master's Thesis

Videos

• Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data

Tuberculosis detection by AI-supported screening

In order to support medical professionals in patient screening to detect tuberculosis infections, VRVis has developed a deep learning-based application in collaboration with Agfa Healthcare. With large numbers of patients, it assists in identifying diseased individuals more rapidly, optimising further diagnosis and treatment courses. 

• Press release

Medical image registration

Medical image registration is an integral part of medical analysis workflows. It allows different images of one or more patients to be analyzed in a directly comparable way by transforming the images into the same coordinate system. Registration is necessary to compare images between studies acquired at different time points or between different modalities.

VRVis has developed methods for classical and deep learning based image registration in collaboration with its medical partners, and in particular investigated how visualization can support registration workflows and highlight problems.

Lastest publications

• A. Berg , E. Vandersmissen , M. Wimmer , D. Major , T. Neubauer , D. Lenis , J. Cant , A. Snoeckx , K. Bühler (2023): Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods. Computers in Biology and Medicine.
• M. Schlachter , T. Fechter , M. Jurisic , T. Schimek-Jasch , O. Oehlke , S. Adebahr , W. Birkfellner , U. Nestle , K. Bühler (2016): Visualization of Deformable Image Registration Quality using Local Image Dissimilarity. IEEE Transactions on Medical Imaging.

Segmentation and annotation of blood vessels

According to the World Health Organization, arteriosclerosis ist the no.1 cause of death globally. A detailed analysis of blood vessels in 3D angiograms is generally very time-consuming. In collaboration with Agfa Healthcare, VRVis has developed several methods for efficient semi-automated and fully automated segmentation of peripheral and coronary blood vessels, as well as their semantic annotation; some of these methods have won awards, been published in high-impact journals, patented and integrated into our customers’ products.

Selection of publications

• A. Novikov, D. Major, M. Wimmer, G. Sluiter, and K. Bühler, “Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans.,” IEEE Trans. Med. Imaging, vol. 36, no. 6, pp. 1359–1371, 2017.

• D. Major, A. Novikov, M. Wimmer, J. Hladuvka, and K. Bühler, “Automated Slice-Based Artery Identification in Various Field-of-View CTA Scans,” presented at the Eurographics Workshop on Visual Computing for Biology and Medicine, 2015, pp. 123–129.

• A. Novikov, M. Wimmer, D. Major, and K. Bühler, “A Two-Level Cascade Classification Algorithm for Real-Time Bifurcation Detection in CTA Images of Blood Vessels.,” presented at the Eurographics Workshop on Visual Computing for Biology and Medicine, 2015.

• S. Zambal, J. Hladuvka, A. Kanitsar, and K. Bühler, “Shape and Appearance Models for Automatic Coronary Artery Tracking,” In MICCAI 2008 Contest: 3D Segmentation in Clinic: A Grand Challange, 2008. (Gewinner in 2 Kategorien)

• P. Felkel, R. Wegenkittl, and K. Bühler, “Surface Models of Tube Trees,” in Proceedings of CGI -Computer Graphics International, pp. pp. 70–77, 2004.

General approaches to feature detection and segmentation of organs

VRVis has developed and patented a number of basic methods for medical image analysis. These include widely cited AI methods for simultaneous segmentation of several objects on radiology images, sequential segmentation methods for CT image data, efficient feature detection methods, as well as several basic methods for statistical shape and texture models which, for example, are still used in combination with AI methods for difficult tasks in organ segmentation.

Selection of publications

• A. Novikov, D. Major, M. Wimmer, D. Lenis, and K. Bühler, “Deep Sequential Segmentation of Organs in Volumetric Medical Scans.,” IEEE Trans. Med. Imaging, vol. 38, no. 5, pp. 1207–1215, May 2019.

• A. Novikov, D. Lenis, D. Major, J. Hladuvka, M. Wimmer, and K. Bühler, “Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs,” IEEE Trans. Med. Imaging, vol. 37, no. 8, pp. 1865–1876, Aug. 2018.

• F. Schulze, D. Major, and K. Bühler, “Fast and Memory Efficient Feature Detection using Multiresolution Probabilistic Boosting Trees.,” Journal of WSCG, vol. 19, no. 1, pp. 33–40, 2011.

• S. Zambal, K. Bühler, and J. Hladuvka, “Entropy-Optimized Texture Models.,” Med Image Comput Comput Assist Interv, vol. 11, no. 2, pp. 213–221, 2008.

• J. Beyer, C. Langer, L. Fritz, S. Wolfsberger, and K. Bühler, “Interactive Diffusion Based Smoothing and Segmentation of Volumetric Datasets on Graphics Hardware,” Journal Methods of Information in Medicine 2007, pp. pp. 270–274, 2007.

• J. Hladuvka and K. Bühler, “MDL Spline Models: Gradients and Polynomial Reparametrizations,” in Proceedings of BMVC - British Machine Vision Conference, pp. pp. 869–878, 2005.

Fully automated semantic annotation of the spinal column

The spine is an important reference system in the human body for describing the site of pathologies during a radiological examination. For this reason, the anatomical name of each visible vertebra in the image must be known and, in many countries, has to be labelled on the image by radiologists manually. This time-consuming work has to be performed by radiologists in addition to the actual diagnosis within a short space of time. VRVis has developed a group of algorithms which can fully automatically recognise and correctly annotate the anatomy of the human vertebrae from different MR and CT scans independent of the image section. This important automated preparatory work simplifies the radiologist’s work and provides more time for the actual diagnosis.

Selection of publications

• D. Major, J. Hladuvka, F. Schulze, and K. Bühler, “Automated Landmarking and Labeling of Fully and Partially Scanned Spinal Columns in CT Images,” Med Image Anal, vol. 17, no. 8, pp. pp. 1151–1163, 2013.

• M. Wimmer, D. Major, A. A. Novikov, and K. Bühler, “Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.,” Int. J. Computer Assisted Radiology and Surgery, vol. 13, no. 10, pp. 1591–1603, 2018.

• J. Hladuvka, D. Major, and K. Bühler, “Bone Profiles: Simple, Fast, and Reliable Spine Localization in CT Scans,” in Lecture Notes in Computational Vision and Biomechanics, vol. 20, no. 15, Cham: Springer International Publishing, 2015, pp. 173–184.

Cardiac analysis

Today, cardiovascular diseases are the most common cause of death in Austria. A detailed analysis of (time-dependent) 3D image data of the beating heart and coronary arteries in a patient is extremely time-consuming. Fully automated methods which simplify the quantification of heart pathologies can therefore make a considerable contribution to accelerating the diagnosis by the radiologist. VRVis has already developed several solutions for segmenting the heart ventricles on CT and MR images as well as an award-winning solution for fully automated segmentation of coronary arteries.

Selection of publications

• A. Grünauer, S. Zambal, and K. Bühler, “Detektion von Koronararterien: Das Beste aus zwei Welten,” in Proceedings of Bildverarbeitung für die Medizin 2011, pp. pp. 269–273, 2011.

• S. Zambal, A. Schöllhuber, K. Bühler, and J. Hladuvka, “Fast and robust localization of the heart in cardiac MRI series,” in Proceedings of the International Conference on Computer Vision Theory and Applications (Visapp 2008), pp. pp. 314–316, 2008.

• S. Zambal, J. Hladuvka, A. Kanitsar, and K. Bühler, “Shape and Appearance Models for Automatic Coronary Artery Tracking,” In MICCAI 2008 Contest: 3D Segmentation in Clinic: A Grand Challange, 2008.

• S. Zambal, J. Hladuvka, and K. Bühler, “A fully automatic system for segmentation and analysis of the left and right ventricles of the heart using a bi-temporal two-component model,” in Proceedings of CARS 2007 (Computer Assisted Radiology Surgery), 2007.

• S. Zambal, J. Hladuvka, and K. Bühler, “Improving Segmentation of the Left Ventricle Using a Two-Component Statistical Model.,” MICCAI, vol. 4190, no. 19, pp. 151–158, 2006.

Several patents on AI

In association with our application-oriented research work for our business partner Agfa Healthcare, many patents for AI-based solutions have been produced in our in-house basic research, for example on the fully automated segmentation and annotation of blood vessels or human vertebrae. You can find out more about our patents.

• Our expertise and publications on the research topic image analysis