TY - GEN
T1 - Uncertainty-aware Brain Lesion Visualization
AU - Gillmann, Christina
AU - Saur, Dorothee
AU - Wischgoll, Thomas
AU - Hoffmann, Karl Titus
AU - Hagen, Hans
AU - Maciejewski, Ross
AU - Scheuermann, Gerik
N1 - Publisher Copyright:
© 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Brain Lesion visualization
KW - Medical visualization
KW - Uncertainty visualization
UR - http://www.scopus.com/inward/record.url?scp=85114715595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114715595&partnerID=8YFLogxK
U2 - 10.2312/vcbm.20201176
DO - 10.2312/vcbm.20201176
M3 - Conference contribution
AN - SCOPUS:85114715595
T3 - Eurographics Workshop on Visual Computing for Biomedicine
SP - 97
EP - 101
BT - EG VCBM 2020 - Eurographics Workshop on Visual Computing for Biology and Medicine, Full and Short Paper Proceedings
A2 - Kozlikova, Barbora
A2 - Krone, Michael
A2 - Smit, Noeska
A2 - Fellner, Dieter W.
A2 - Hansmann, Werner
A2 - Purgathofer, Werner
A2 - Sillion, Francois
PB - Eurographics Association
T2 - 10th Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2020
Y2 - 28 September 2020 through 1 October 2020
ER -