How an artificial intelligence-based classification algorithm assigns a category, such as ‘diseased’ or ‘healthy’, to a radiological image, is typically not intelligible for the user. The therefore predominantly used neuronal networks behave like a black box – the learned image features guiding the networks decision remain elusive for the user.

For years, VRVis has been dedicated to researching AI solutions that are not only explainable and intuitive, but also reliable, producing comprehensible and trustworthy decisions. To achieve this goal, we are developing methods that make the neural network's decision-making process transparent to the user. These methods not only give medical professionals additional feedback on the reliability of the decision but also help developers in verifying the quality of the trained network.

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.

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.

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.

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.

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.

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.