Share Email Print
cover

Proceedings Paper

Surgical motion characterization in simulated needle insertion procedures
Author(s): Matthew S. Holden; Tamas Ungi; Derek Sargent; Robert C. McGraw; Gabor Fichtinger
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

PURPOSE: Evaluation of surgical performance in image-guided needle insertions is of emerging interest, to both promote patient safety and improve the efficiency and effectiveness of training. The purpose of this study was to determine if a Markov model-based algorithm can more accurately segment a needle-based surgical procedure into its five constituent tasks than a simple threshold-based algorithm. METHODS: Simulated needle trajectories were generated with known ground truth segmentation by a synthetic procedural data generator, with random noise added to each degree of freedom of motion. The respective learning algorithms were trained, and then tested on different procedures to determine task segmentation accuracy. In the threshold-based algorithm, a change in tasks was detected when the needle crossed a position/velocity threshold. In the Markov model-based algorithm, task segmentation was performed by identifying the sequence of Markov models most likely to have produced the series of observations. RESULTS: For amplitudes of translational noise greater than 0.01mm, the Markov model-based algorithm was significantly more accurate in task segmentation than the threshold-based algorithm (82.3% vs. 49.9%, p<0.001 for amplitude 10.0mm). For amplitudes less than 0.01mm, the two algorithms produced insignificantly different results. CONCLUSION: Task segmentation of simulated needle insertion procedures was improved by using a Markov model-based algorithm as opposed to a threshold-based algorithm for procedures involving translational noise.

Paper Details

Date Published: 17 February 2012
PDF: 6 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83160W (17 February 2012); doi: 10.1117/12.911003
Show Author Affiliations
Matthew S. Holden, Queen's Univ. (Canada)
Tamas Ungi, Queen's Univ. (Canada)
Derek Sargent, Queen's Univ. (Canada)
Robert C. McGraw, Queen's Univ. (Canada)
Gabor Fichtinger, Queen's Univ. (Canada)


Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes III; Kenneth H. Wong, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray