Evaluation of Different Visualization Techniques for Perception-Based Alignment in Medical AR
Many augmented reality (AR) applications require the alignment of virtual objects to the real world; this is particularly important in medical AR scenarios where medical imaging information may be displayed directly on a patient and is used to identify the exact locations of specific anatomical structures within the body.
In this paper, we explore how different static visualization techniques influence users' ability to perform perception-based alignment in AR for breast reconstruction surgery, where surgeons must accurately identify the locations of several perforator blood vessels while planning the procedure. We conducted a pilot study in which four subjects used four different visualization techniques with varying degrees of opaqueness and brightness as well as outline contrast to align virtual replicas of the relevant anatomy to their 3D-printed counterparts. Results indicate that the highest source of alignment error was along the depth dimension, with users consistently overestimating depth when aligning the virtual renderings. The majority of subjects preferred visualization techniques rendered with lower levels of opaqueness and brightness as well as higher outline contrast, which were also found to support more accurate alignment.
Fischer M, Leuze C, Perkins S, Rosenberg J, Daniel B, Martin-Gomez A. Evaluation of different visualization techniques for perception-based alignment in medical AR. 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Recife, Brazil, 2020, pp. 45-50, doi: 10.1109/ISMAR-Adjunct51615.2020.00027.
Marc Fischer is an alumnus of the IMMERS group