VRVis researcher Theresa Neubauer receives OCG Promotion Award 2022
OCG Promotion Award winner Theresa Neubauer (center) with VRVis scientific director Katja Bühler and researcher Maria Wimmer, who supervised the honored master's thesis at VRVis.
Theresa Neubauer entwickelte in Diplomarbeit "Volumetric Tumor Segmentation on Multimodal Medical Images using Deep Learning" eine neue Methode, durch die multimodale Informationen verschiedener bildgebender Verfahren für einen ganzheitlichen, optimierten medizinischen Diagnose-Workflow kombiniert werden können.
This year, there were more submissions for the OCG Promotion Award than ever before. Due to the high quality of the submitted master's theses, one researcher and two researchers were awarded this year. From left to right: Martin Plattner, University of Innsbruck; OCG Promotion Award Jury Chair Gabriele Kotsis; Theresa Neubauer, VRVis and Fabio Francisco Oberweger, TU Vienna. (c) Irina Scheitz/OCG
OCG is committed to promoting the use of human-centered information technologies for the benefit of society, a principle that has always been of great importance in the selection of OCG Promtion Award winners. On behalf of the renowned jury, Prof. Gabriele Kotsis presented the award certificate to VRVis researcher Theresa Neubauer. (c) Irina Scheitz/OCG
The OCG Promotion Award was presented on August 22, 2022, during the DEXA - International Conference on Database and Expert Systems Applications, which took place this year in Vienna.
Her master thesis, which resulted from a research project of our Biomedical Image Informatics Group, is a great example of best practice in promoting young scientists as well as in interdisciplinary applied research: Under the direction of Katja Bühler and supervised by Maria Wimmer, VRVis was the scientific home of Theresa Neubauer, from where she advanced her research work in close cooperation with her scientific mentors Prof. Thomas Beyer from MedUni Vienna and Eduard Gröller from TU Wien. Theresa Neubauer's research focused on using innovative visual computing and AI techniques to merge multimodal images automatically for more efficient tumor detection to achieve holistic diagnostics.
More on Theresa Neubauer's distinguished master's thesis
Soft tissue tumors are tumors of various tissues (musculature, connective tissue, fatty tissue, nerve tissue). An essential part of their diagnosis and the determination of a successful therapy is the correct assessment of the tumors on medical scans. To do this, medical professionals visually analyze the tumor on MR, CT, and PET scans. These images thereby capture the tumor in different anatomical, functional and molecular contexts and thus also provide different information about the tumor, because different aspects and images are relevant for different clinical questions - e.g. biopsy, radiotherapy, or surgical planning. Until now, tumor segmentation has been a manual and time-consuming task requiring a great deal of radiologists' attention. Here, automated tools can be a valuable addition to everyday clinical practice.
However, to date, the potential of multi-modal data for tumor detection has only been exploited by a few established computer-aided image segmentation methods. In order to make a significant contribution here, Theresa Neubauer, an expert in the field of medical informatics, has developed a new method in the course of her diploma thesis "Volumetric Tumor Segmentation on Multi-modal Medical Images using Deep Learning", by means of which multi-modal information from different imaging procedures can be combined for a holistic, optimized diagnostic workflow. This involves fusing magnetic resonance, computed tomography, and positron emission tomography data to obtain a more accurate picture of the tumor. The image segmentation method developed uses machine learning (artificial intelligence) to learn complex image features and relationships between modalities to segment the tumor more efficiently and accurately.