A brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed for brain lesions. Our method is based on an uncertainty measure for image data that forms the input of an uncertainty-aware segmentation approach. Here, medical doctors can determine the lesion in the patient’s brain and the result can be visualized by an uncertainty-aware geometry rendering. We applied our approach to two patient datasets to review the lesions. Our results indicate increased knowledge discovery in brain lesion analysis that provides a quantification of trust in the generated results.

Original languageEnglish (US)
Title of host publicationEG VCBM 2020 - Eurographics Workshop on Visual Computing for Biology and Medicine, Full and Short Paper Proceedings
EditorsBarbora Kozlikova, Michael Krone, Noeska Smit, Dieter W. Fellner, Werner Hansmann, Werner Purgathofer, Francois Sillion
PublisherEurographics Association
Number of pages5
ISBN (Electronic)9783038681090
StatePublished - 2020
Event10th Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2020 - Tubingen, Germany
Duration: Sep 28 2020Oct 1 2020

Publication series

NameEurographics Workshop on Visual Computing for Biomedicine
ISSN (Print)2070-5778
ISSN (Electronic)2070-5786


Conference10th Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2020


  • Brain Lesion visualization
  • Medical visualization
  • Uncertainty visualization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering


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