Eden Bibic

Visiting Researcher

School of Computing

Queen’s University

Member from 2016 to 2017

Eden was a high school intern from Leahurst College on the Queen’s University Internships in Computing (QUIC) during the summer of 2016 and then she continued working in the Perk Lab throughout the year.

Harish, Vinyas; Bibic, Eden; Lasso, Andras; Holden, M.; Vaughan, Thomas; Baum, Zachary M C; Ungi, Tamas; Fichtinger, Gabor

Monitoring electromagnetic tracking error using redundant sensors Conference

SPIE Medical Imaging 2017, SPIE Society for Optics and Photonics SPIE Society for Optics and Photonics, Orlando, FL, USA, 2017.

Abstract | Links | BibTeX

@conference{Harish2017a,

title = {Monitoring electromagnetic tracking error using redundant sensors},

author = {Vinyas Harish and Eden Bibic and Andras Lasso and M. Holden and Thomas Vaughan and Zachary M C Baum and Tamas Ungi and Gabor Fichtinger},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Harish2017a.pdf},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {SPIE Medical Imaging 2017},

publisher = {SPIE Society for Optics and Photonics},

address = {Orlando, FL, USA},

organization = {SPIE Society for Optics and Photonics},

abstract = {<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p><span style="font-family:trebuchet ms,helvetica,sans-serif"><span style="font-size:12px"><strong>PURPOSE: </strong>The intraoperative measurement of tracking error is crucial to ensure the reliability of electromagnetically navigated procedures. For intraoperative use, methods need to be quick to set up, easy to interpret, and not interfere with the ongoing procedure. Our goal was to evaluate the feasibility of using redundant electromagnetic sensors to alert users to tracking error in a navigated intervention setup. <strong>METHODS: </strong>Electromagnetic sensors were fixed to a rigid frame around a region of interest and on surgical tools. A software module was designed to detect tracking error by comparing real-time measurements of the differences between inter-sensor distances and angles to baseline measurements. Once these measurements were collected, a linear support vector machine-based classifier was used to predict tracking errors from redundant sensor readings. <strong>RESULTS: </strong>Measuring the deviation in the reported inter-sensor distance and </span></span><span style="font-family:timesnewromanpsmt; font-size:10.000000pt"><span style="font-family:lucida sans unicode,lucida grande,sans-serif"><span style="font-size:10px"><span style="font-size:12px"><span style="font-family:arial,helvetica,sans-serif"><span style="font-family:trebuchet ms,helvetica,sans-serif">angle</span></span></span></span></span></span><span style="font-family:trebuchet ms,helvetica,sans-serif"><span style="font-size:12px"> between the needle and cautery served as a valid indicator for electromagnetic tracking error. The highest classification accuracy, 86%, was achieved based on readings from the cautery when the two sensors on the cautery were close together. The specificity of this classifier was 93% and the sensitivity was 82%. <strong>CONCLUSION: </strong>Placing redundant electromagnetic sensors in a workspace seems to be feasible for the intraoperative detection of electromagnetic tracking error in controlled environments. Further testing should be performed to optimize the measurement error threshold used for classification in the support vector </span></span><span style="font-family:timesnewromanpsmt; font-size:10.000000pt"><span style="font-family:lucida sans unicode,lucida grande,sans-serif"><span style="font-size:10px"><span style="font-size:12px"><span style="font-family:arial,helvetica,sans-serif"><span style="font-family:trebuchet ms,helvetica,sans-serif">machine,</span></span></span></span></span></span><span style="font-family:trebuchet ms,helvetica,sans-serif"><span style="font-size:12px"> and improve the sensitivity of our method before application in real procedures. </span></span></p> 

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</div> 

</div>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> PURPOSE: The intraoperative measurement of tracking error is crucial to ensure the reliability of electromagnetically navigated procedures. For intraoperative use, methods need to be quick to set up, easy to interpret, and not interfere with the ongoing procedure. Our goal was to evaluate the feasibility of using redundant electromagnetic sensors to alert users to tracking error in a navigated intervention setup. METHODS: Electromagnetic sensors were fixed to a rigid frame around a region of interest and on surgical tools. A software module was designed to detect tracking error by comparing real-time measurements of the differences between inter-sensor distances and angles to baseline measurements. Once these measurements were collected, a linear support vector machine-based classifier was used to predict tracking errors from redundant sensor readings. RESULTS: Measuring the deviation in the reported inter-sensor distance and angle between the needle and cautery served as a valid indicator for electromagnetic tracking error. The highest classification accuracy, 86%, was achieved based on readings from the cautery when the two sensors on the cautery were close together. The specificity of this classifier was 93% and the sensitivity was 82%. CONCLUSION: Placing redundant electromagnetic sensors in a workspace seems to be feasible for the intraoperative detection of electromagnetic tracking error in controlled environments. Further testing should be performed to optimize the measurement error threshold used for classification in the support vector machine, and improve the sensitivity of our method before application in real procedures. 
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Harish, Vinyas; Bibic, Eden; Lasso, Andras; Holden, M.; Vaughan, Thomas; Baum, Zachary M C; Ungi, Tamas; Fichtinger, Gabor

An application of redundant sensors for intraoperative electromagnetic tracking error monitoring Conference

15th Annual Imaging Network Ontario Symposium, London, ON, Canada, 2017.

Links | BibTeX

Bibic, Eden; Baum, Zachary M C; Harish, Vinyas; Ungi, Tamas; Lasso, Andras; Fichtinger, Gabor

PLUS Model Catalog: A library of 3D-printable models Conference

15th Annual Imaging Network Ontario Symposiuim, Imaging Network Ontario (ImNO), London, Canada, 2017.

Links | BibTeX