Evaluating surface visualization methods in semi-transparent volume rendering in virtual reality

ABSTRACT

Perceptual visualisation of semi-transparent structures in volumetric datasets is challenging due to its inherent visual complexity. This is however of primary importance in medical visualisation where raymarching of volumetric data is common. While rendering volumetric data itself is a well-explored area, perception of volume-rendered images combined with semi-transparent mesh data remains an underexplored topic. As virtual reality (VR) is increasingly employed for medical data inspection, it becomes important to understand how different mesh visualisations affect performance in medical tasks in this context. When considering direct volume rendering in immersive VR, the effects of stereoscopic vision and motion parallax require additional consideration.

We investigate how surface transparency modes affect task performance in VR. For two medical image analysis tasks, we conduct a user study (n = 23) to analyse the impact of mesh rendering methods on task outcome and subjective preference. Our evaluation indicates that user performance with wireframe rendering varies greatly between tasks while constant opacity and silhouette provide a stable benefit. Overall, transparent visualisations led to improved precision (lower error rate, lower inter-observer variability) and increased user confidence, while the efficiency of visualisation methods was task-dependent. This demonstrates how semi-transparent rendering will improve visual analysis in medical applications and other disciplines$n=23$.

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Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Intra- and inter-observer variability are standard measurement parameters of precision and reproducibility in the context of valve dimensions, see Knobloch et al. (2018); Heo et al. (2014).

Additional information

Notes on contributors

Gloria Zörnack

Gloria Zörnack received her B.Sc. in Physics and M.Sc. in Biomedical Computing from the Technical University of Munich in 2016 and 2019. Her master's thesis she wrote at TOMTEC Imaging Systems about web-based VR for Cardiac 3D ultrasound. Since December 2019 she is employed as Product Manager and VR Engineer at Virtonomy GmbH in Munich, Germany. Her research interests include medical volume processing and rendering and multi-user applications for medical research, including virtual and augmented reality technologies.

Jakob Weiss

Jakob Weiss is currently employed as a Visualization Specialist at OneProjects Design and Innovation GmbH in Munich, Germany. He has received his B. Sc. and  M. Sc. in Informatics from the Technical University of Munich in 2013 and 2016, respectively, along with a B. Sc. in Games Engineering in 2016. Between 2016 and 2020, he worked as a research scientist and doctoral candidate with the TUM Chair for Computer Aided Medical Procedures (CAMP). His research is focused on visual computing to improve guidance systems for medical applications, including topics like medical volume processing and rendering, computer graphics, augmented and virtual reality.

Georg Schummers

Georg Schummers is currently employed as a Senior Scientist at TOMTEC Imaging Systems GmbH. TOMTEC provides innovative medical imaging and information management software with a focus on ultrasound.

Ulrich Eck

Ulrich Eck was born in Munich, Germany. He finished his PhD in Computer and Information Science at the Magic Vision Lab of University of South Australia. Now he is leading the research activities at the NARVIS laboratory of the Chair for Computer Aided Medical Procedures and Augmented Reality at the Technical University in Munich. His research area is Multi-Modal Augmented Reality systems for Medical Procedures, Training, and Education.

Nassir Navab

Nassir Navab is a Full Professor and Director of the Laboratory for Computer-Aided Medical Procedures at Johns Hopkins University and the Technical University of Munich. He has also secondary faculty appointments at both affiliated Medical Schools. He completed his PhD at INRIA and University of Paris XI, France, and enjoyed two years of a post-doctoral fellowship at MIT Media Laboratory before joining Siemens Corporate Research (SCR) in 1994. At SCR, he was a distinguished member and received the Siemens Inventor of the Year Award in 2001. He received the SMIT Society Technology award in 2010 for the introduction of Camera Augmented Mobile C-arm and Freehand SPECT technologies, and the ‘10 years lasting impact award’ of IEEE ISMAR in 2015. In 2012, he was elected as a Fellow of the MICCAI Society. He has acted as a member of the board of directors of the MICCAI Society, 2007-2012 and 2014-2017, and serves on the Steering committee of the IEEE Symposium on Mixed and Augmented Reality (ISMAR) and Information Processing in Computer-Assisted Interventions (IPCAI). He is the author of hundreds of peer-reviewed scientific papers, with more than 36,000 citations and an h-index of 88 as of November, 2020. He is the author of more than thirty awarded papers including 11 at MICCAI, 5 at IPCAI, and 3 at IEEE ISMAR. He is the inventor of 50 granted US patents and more than 50 International ones. His current research interests include medical augmented reality, computer-aided surgery, medical robotics, and machine learning.