EG 2017 - Dirk Bartz Prize

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https://diglib.eg.org/handle/10.2312/2631251

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Browsing EG 2017 - Dirk Bartz Prize by Subject "J.3 [Computer Applications]"

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    Sketching and Annotating Vascular Structures to Support Medical Teaching, Treatment Planning and Patient Education
    (The Eurographics Association, 2017) Saalfeld, Patrick; Oeltze-Jafra, Steffen; Saalfeld, Sylvia; Preim, Uta; Beuing, Oliver; Preim, Bernhard; Stefan Bruckner and Timo Ropinski
    In clinical practice, hand drawn sketches are employed to express concepts and are an efficient method for the discussion of complex issues. We present computer graphic methods to improve and support the creation and annotation of complex sketches, resulting in a more clear, expressive and understandable result. For this, we consider the medical areas of teaching, treatment planning and patient education. Our applications allow students, educators, physicians and patients to sketch and annotate vascular structures, their pathologies and treatment options as well as to simulate and illustrate blood flow. The used sketching approaches take advantage of semi-immersive environments as well as interactive whiteboards to enable the creation of vessels either in their spatially complex 3D representation or as a simplified 2D illustration. We evaluate our work in interviews with physicians and user studies to assess their usability and to reveal their benefits to support the respective medical domain.
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    Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline
    (The Eurographics Association, 2017) Raidou, Renata G.; Breeuwer, Marcel; Vilanova, Anna; Stefan Bruckner and Timo Ropinski
    Prostate cancer is one of the most frequently occurring types of cancer in males. It is often treated with radiation therapy, which aims at irradiating tumors with a high dose, while sparing the surrounding healthy tissues. In the course of the years, radiotherapy technology has undergone great advancements. However, tumors are not only different from each other, they are also highly heterogeneous within, consisting of regions with distinct tissue characteristics, which should be treated with different radiation doses. Tailoring radiotherapy planning to the specific needs and intra-tumor tissue characteristics of each patient is expected to lead to more effective treatment strategies. Currently, clinical research is moving towards this direction, but an understanding of the specific tumor characteristics of each patient, and the integration of all available knowledge into a personalizable radiotherapy planning pipeline are still required. The present work describes solutions from the field of Visual Analytics, which aim at incorporating the information from the distinct steps of the personalizable radiotherapy planning pipeline, along with eventual sources of uncertainty, into comprehensible visualizations. All proposed solutions are meant to increase the - up to now, limited - understanding and exploratory capabilities of clinical researchers. These approaches contribute towards the interactive exploration, visual analysis and understanding of the involved data and processes at different steps of the radiotherapy planning pipeline, creating a fertile ground for future research in radiotherapy planning.