Speaker: Eric Mörth (Visualization Group, University of Bergen )
Multiparametric imaging in cancer has been shown to be useful for tumor detection. Furthermore, radiomic tumor profiling enables a deeper analysis of tumor phenotypes and enables analysis of a possible link to aggressiveness of the tumor. Analyzing complex imaging data in combination with clinical data is not trivial. We enable clinical experts to gather new insights of their multiparametric imaging data as well as cohort data. We include more than 7 modalities in a single view as well as cohort data of more than 100 endometrial cancer patients, including manually performed tumor segmentations. The goal of our contributions is to enable medical experts to obtain deeper understanding of different tumor types to define individual treatment for each patient.
Supporting the communication in science as well as between doctors and patients is another challenging task and one of our goals. In our latest contribution we propose a novel approach for authoring, editing, and presenting data-driven scientific narratives using scrollytelling. Our method flexibly integrates common sources such as images, text, and video, but also supports more specialized visualization techniques such as interactive maps or scalar field visualizations.
In this talk, I will present our efforts to scale up medical visualization supporting multi-modal, multi-patient and multi-audience approaches for healthcare data analysis and communication.
