Using registration uncertainty visualization in a user study of a simple surgical task

Authors:
Amber L. Simpson;Burton Ma;Elvis C. S. Chen;Randy E. Ellis;A. James Stewart
Affiliations:
School of Computing, Queen’s University, Kingston, Canada;School of Computing, Queen’s University, Kingston, Canada;School of Computing, Queen’s University, Kingston, Canada;School of Computing, Queen’s University, Kingston, Canada;School of Computing, Queen’s University, Kingston, Canada
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

We present a novel method to visualize registration uncertainty and a simple study to motivate the use of uncertainty visualization in computer–assisted surgery. Our visualization method resulted in a statistically significant reduction in the number of attempts required to localize a target, and a statistically significant reduction in the number of targets that our subjects failed to localize. Most notably, our work addresses the existence of uncertainty in guidance and offers a first step towards helping surgeons make informed decisions in the presence of imperfect data.