@article {Holden2019a, title = {Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions}, journal = {International Journal of Computer Assisted Radiology and Surgery}, year = {In Press}, doi = {https://doi.org/10.1007/s11548-019-01977-3}, author = {M. Holden and Sean Xia and H. Lia and Keri, Zsuzsanna and Colin Bell and Lindsey Patterson and Tamas Ungi and Gabor Fichtinger} } @article {Lee2022a, title = {Evaluating Faster R-CNN for cataract surgery tool detection using microscopy video}, year = {2022}, abstract = {

Introduction: Traditional methods of cataract surgery skill assessment rely on human expert supervision. This exposes the trainee to interobserver variability and inconsistent feedback. Alternative measures such as sensorbased instrument motion analysis promise objective assessment [1]. However, sensor-based systems are logistically complicated and expensive to obtain. Previous studies have demonstrated a strong correlation between sensor-based metrics and two-dimensional motion metrics obtained from object detection [2]. Reliable object detection is the foundation for computing such performance metrics. Therefore, the objective of this study is to evaluate the performance of an object detection network, namely Faster Region-Based Convolutional Neural Network (FRCNN), in recognition of cataract surgery tools in microscopy video. Methods: Microscope video was recorded for 25 trials of cataract surgery on an artificial eye. The trials were performed by a cohort consisting of one senior-surgeon and four junior-surgeons and manually annotated for bounding box locations of the cataract surgery tools (Figure 1) The surgical tools used included: forceps, diamond keratomes, viscoelastic cannulas, and cystotome needles. A FRCNN [3] was trained on a total of 130,614 frames for object detection. We used five-fold cross validation, using a leave-one-userout method. In this manner, all videos from one surgeon were reserved for testing and the frames from the remaining 20 videos were divided among training and validation. Network performance was evaluated via mean average precision (mAP), which is defined as the area under the precision/recall curve. Samples were considered correctly identified when the intersection over union (IoU) between the ground truth and predicted bounding boxes was greater than 0.5. Results: The overall mAP of the network was 0.63. Toolspecific mAPs ranged between 0.49 and 0.96 (Table 1). The high accuracy in detection of the cystotome needle is likely due to the distinct size and shape of the tool tip. The diamond keratome had the lowest mAP of any of the tools recognized, however this may be attributed to variations in the appearance of the tool tip (Figure 2). Conclusions: The FRCNN was able to recognize the surgical tools used in cataract surgery with reasonably high accuracy. Now that we know the network can sufficiently recognize the surgical tools, our next goal is to use this network to compute motion-based performance metrics. Future work seeks to validate these performance metrics against those obtained from sensor-based tracking and against expert evaluations. This serves as a first step towards providing consistent and accessible feedback for future trainees learning cataract surgery.\ 

}, author = {H.Y. Lee}, editor = {R. Hisey} } @conference {ODriscoll2022a, title = {Feasibility of object detection for skill assessment in central venous catheterization}, booktitle = {SPIE Medical Imaging}, year = {2022}, month = {02/2022}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego}, abstract = {

Purpose: Computer-assisted surgical skill assessment methods have traditionally relied on tracking tool motion with physical sensors. These tracking systems can be expensive, bulky, and impede tool function. Recent advances in object detection networks have made it possible to quantify tool motion using only a camera. These advances open the door for a low-cost alternative to current physical tracking systems for surgical skill assessment. This study determines the feasibility of using metrics computed with object detection by comparing them to widely accepted metrics computed using traditional tracking methods in central venous catheterization. Methods: Both video and tracking data were recorded from participants performing central venous catheterization on a venous access phantom. A Faster Region-Based Convolutional Neural Network was trained to recognize the ultrasound probe and syringe on the video data. Tracking-based metrics were computed using the Perk Tutor extension of 3D Slicer. The path length and usage time for each tool were then computed using both the video and tracking data. The metrics from object detection and tracking were compared using Spearman rank correlation. Results: The path lengths had a rank correlation coefficient of 0.22 for the syringe (p\<0.03) and 0.35 (p\<0.001) for the ultrasound probe. For the usage times, the correlation coefficient was 0.37 (p\<0.001) for the syringe and 0.34 (p\<0.001) for the ultrasound probe. Conclusions: The video-based metrics correlated significantly with the tracked metrics, suggesting that object detection could be a feasible skill assessment method for central venous catheterization.

}, keywords = {central venous catheterization, computer-assisted skill assessment, Object detection}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger} } @article {ODriscoll2022b, title = {Feasibility of using object detection for performance assessment in central venous catherization}, year = {2022}, month = {02/2022}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger} } @conference {Laframboise2019a, title = {Analyzing the curvature of the colon in different patient positions}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, author = {Jacob Laframboise and Tamas Ungi and Andras Lasso and Mark Asselin and M. Holden and Tan, Pearl and Lawrence Hookey and Gabor Fichtinger} } @article {Laframboise2019b, title = {Quantifying the effect of patient position on the curvature of colons}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, keywords = {colonoscopy, CT, curvature, curves}, author = {Jacob Laframboise and Tamas Ungi and Andras Lasso and Mark Asselin and M. Holden and Tan, Pearl and Lawrence Hookey and Gabor Fichtinger} } @article {GarciaMato2018a, title = {3D Gaze tracking for skill assessment in ultrasound-guided needle insertions}, volume = {13}, year = {2018}, month = {06/2018}, pages = {S52-S53}, publisher = {Springer}, address = {Berlin, Germany}, doi = {https://doi.org/10.1007/s11548-018-1766-y}, author = {D. Garc{\'\i}a-Mato and M. Holden and Andras Lasso and A. Szulewski and Javier Pascau and Gabor Fichtinger} } @article {Rae2018b, title = {Accuracy of the Microsoft HoloLens for neurosurgical burr hole placement}, year = {2018}, address = {Toronto, Canada}, author = {Emily Rae and Andras Lasso and M. Holden and Evelyn Morin and Ron Levy and Gabor Fichtinger} } @article {Hisey2018b, title = {Assessment of the use of webcam based workflow detection for providing real-time feedback in central venous catheterization training}, year = {2018}, author = {R. Hisey and Tamas Ungi and M. Holden and Zachary M C Baum and Keri, Zsuzsanna and Caitlin McCallum and Daniel Howes and Gabor Fichtinger} } @article {Holden2018b, title = {Configurable Overall Skill Assessment in Ultrasound-Guided Needle Insertion}, year = {2018}, month = {03/2018}, address = {Toronto, Canada}, author = {M. Holden and H. Lia and Sean Xia and Keri, Zsuzsanna and Tamas Ungi and Gabor Fichtinger} } @conference {Emily2018a, title = {Neurosurgical burr hole placement using the Microsoft HoloLens}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10576}, year = {2018}, month = {03/2018}, pages = {105760T}, address = {Houston, Texas}, doi = {10.1117/12.2293680}, author = {Emily Rae and Andras Lasso and M. Holden and Evelyn Morin and Ron Levy and Gabor Fichtinger} } @article {Holden2018a, title = {Objective assessment of colonoscope manipulation skills in colonoscopy training}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {13}, number = {1}, year = {2018}, month = {01/2018}, pages = {105-114}, doi = {https://doi.org/10.1007/s11548-017-1676-4}, author = {M. Holden and Chang Wang and Kyle MacNeil and Ben Church and Lawrence Hookey and Gabor Fichtinger and Tamas Ungi} } @conference {Hisey2018, title = {Real-time workflow detection using webcam video for providing real-time feedback in central venous catheterization training}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2018}, author = {R. Hisey and Tamas Ungi and M. Holden and Zachary M C Baum and Keri, Zsuzsanna and Caitlin McCallum and Daniel Howes and Gabor Fichtinger} } @article {Holden2018c, title = {Self-guided training for deep brain stimulation planning using objective assessment}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {13}, number = {7}, year = {2018}, month = {07/2018}, pages = {1129-1139}, doi = {https://doi.org/10.1007/s11548-018-1753-3}, author = {M. Holden and Yulong Zhao and Claire Haegelen and Caroline Essert and Sara Fernanez-Vidal and Eric Bardinet and Tamas Ungi and Gabor Fichtinger and Pierre Jannin} } @article {Harish2017b, title = {An application of redundant sensors for intraoperative electromagnetic tracking error monitoring}, year = {2017}, address = {London, ON, Canada}, author = {Vinyas Harish and E Bibic and Andras Lasso and M. Holden and Thomas Vaughan and Zachary M C Baum and Tamas Ungi and Gabor Fichtinger} } @article {Holden2017a, title = {Assessing Technical Competency in Simulated Colonoscopy Using Joint Motion Analysis}, year = {2017}, month = {03/2017}, address = {London, Ontario}, author = {M. Holden and Chang Wang and Kyle MacNeil and Ben Church and Lawrence Hookey and Gabor Fichtinger and Tamas Ungi} } @conference {Harish2017a, title = {Monitoring electromagnetic tracking error using redundant sensors}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, publisher = {SPIE Society for Optics and Photonics}, organization = {SPIE Society for Optics and Photonics}, address = {Orlando, FL, USA}, abstract = {

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.\ 

}, 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} } @conference {Holden2017b, title = {Overall Proficiency Assessment in Point-of-Care Ultrasound Interventions: The Stopwatch is not Enough}, booktitle = {Point-of-Care Ultrasound Workshop}, volume = {LNCS 10549}, year = {2017}, month = {09/2017}, pages = {146-153}, publisher = {Springer}, organization = {Springer}, address = {Quebec City, QC}, author = {M. Holden and Keri, Zsuzsanna and Tamas Ungi and Gabor Fichtinger} } @article {Holden2017c, title = {Reducing Cost and Complexity in Computer-Assisted Training for Lumpectomy}, year = {2017}, month = {11/2017}, address = {Kingston, Canada}, author = {M. Holden and Keri, Zsuzsanna and Tamas Ungi and Caitlin Yeo and Justine Ring and Gabor Fichtinger and Boris Zevin} } @article {Bell2017a, title = {Sonographic accuracy as a novel tool for point of care ultrasound competency assessment}, journal = {Academic Emergency Medicine Education and Training}, volume = {1}, number = {4}, year = {2017}, month = {10/2017}, pages = {316-324}, author = {Colin Bell and Conor McKaigney and M. Holden and Gabor Fichtinger and Louise Rang} } @conference {Lia2017, title = {Training with Perk Tutor improves ultrasound-guided in-plane needle insertion skill}, booktitle = {SPIE Medical Imaging}, volume = {10135}, year = {2017}, address = {Orlando, FL}, abstract = {

PURPOSE: The open-source Perk Tutor training platform has been shown to improve trainee performance in interventions that require ultrasound guidance. Our goal was to determine if needle coordination of medical trainees can be improved by training with Perk Tutor compared to training with ultrasound only.\ 

METHODS: Twenty participants with no previous experience were randomized into two groups; the Perk Tutor group and the Control group. The Perk Tutor group had access to the 3D visualization while the Control group used ultrasound only during their training. Performance was analyzed, measured and compared by Perk Tutor with regards to four needle coordination metrics. None of the groups had access to 3D visualization during performance testing.\ 

RESULTS: The needle tracking measurements showed, for the Perk Tutor group, lower average distance between the needle tip and ultrasound (1.2 [0.9 {\textendash} 2.8] mm vs 2.7 [2.3 {\textendash} 4.0] mm, respectively; P = 0.023) and lower maximum distance between the needle tip and ultrasound (2.2 [1.9 {\textendash} 3.2] mm vs 4.6 [3.9 {\textendash} 6.2] mm, respectively; P = 0.013). There was no significant difference in average needle to ultrasound plane angle and maximum needle to ultrasound plane distance. All participants were successful in the procedure.\ 

CONCLUSION: The Perk Tutor group had significantly reduced distance from the needle tip to the ultrasound plane. Training with Perk Tutor can improve trainees{\textquoteright} needle and ultrasound coordination.\ 

}, author = {H. Lia and Keri, Zsuzsanna and M. Holden and Vinyas Harish and Mitchell, Christopher H and Tamas Ungi and Gabor Fichtinger} } @article {Holden2016a, title = {Central Venous Catheterization Curriculum Development via Objective Performance Metrics}, year = {2016}, month = {Submitted}, address = {Toronto, Canada}, author = {M. Holden and Mark Woodcroft and Tim Chaplin and Louise Rang and Melanie Jaeger and Nici Rocca and McGraw, Robert C and Gabor Fichtinger and Tamas Ungi} } @article {McGraw2015a, title = {Development and evaluation of a simulation-based curriculum for ultrasound guided central venous catheterization}, journal = {Canadian Journal of Emergency Medicine}, volume = {18}, number = {6}, year = {2016}, month = {11/2016}, pages = {405-413}, doi = {10.1017/cem.2016.329}, author = {McGraw, Robert C and Tim Chaplin and Conor McKaigney and Louise Rang and Melanie Jaeger and Damian Redfearn and Colleen Davison and Tamas Ungi and M. Holden and Yeo, Caitlin T and Keri, Zsuzsanna and Gabor Fichtinger} } @article {MacNeil2016a, title = {System for objectively evaluating colonoscopy procedural skills using motion analysis}, year = {2016}, month = {Submitted}, address = {Toronto, Canada}, author = {Kyle MacNeil and Chang Wang and M. Holden and Tamas Ungi and Gabor Fichtinger and Lawrence Hookey} } @conference {Carter2016a, title = {Visual feedback mounted on surgical tool: proof of concept}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, month = {In Press}, address = {San Diego, CA, USA}, author = {Kaci Carter and Thomas Vaughan and M. Holden and Gabrielle Gauvin and Padina Pezeshki and Andras Lasso and Tamas Ungi and Evelyn Morin and John Rudan and C. Jay Engel and Gabor Fichtinger} } @article {Clinkard2015a, title = {Assessment of Lumbar Puncture Skill in Experts and Nonexperts Using Checklists and Quantitative Tracking of Needle Trajectories: Implications for Competency-Based Medical Education}, journal = {Teaching and Learning in Medicine}, volume = {27}, number = {1}, year = {2015}, month = {01/2015}, pages = {51-56}, doi = {10.1080/10401334.2014.979184}, author = {David Clinkard and Eric Moult and M. Holden and Colleen Davison and Tamas Ungi and Gabor Fichtinger and McGraw, Robert C} } @article {Keri2015a, title = {Computerized training system for ultrasound-guided lumbar puncture on abnormal spine models: a randomized controlled trial}, journal = {Canadian Journal of Anesthesia}, volume = {62}, year = {2015}, month = {07/2015}, pages = {777-784}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25804431}, author = {Keri, Zsuzsanna and Devin Sydor and Tamas Ungi and M. Holden and McGraw, Robert C and Parvin Mousavi and Daniel P. Borschneck and Gabor Fichtinger and Melanie Jaeger} } @article {Clinkard2015b, title = {The Development and Validation of Hand Motion Analysis to Evaluate Competency in Central Line Catheterization}, journal = {Academic Emergency Medicine}, volume = {22}, number = {2}, year = {2015}, month = {02/2015}, pages = {212-218}, doi = {10.1111/acem.12590}, author = {David Clinkard and M. Holden and Tamas Ungi and David Messenger and Gabor Fichtinger and McGraw, Robert C} } @article {Yeo2015a, title = {Examination of learning trajectories for simulated lumbar puncture training using hand motion analysis}, journal = {Academic Emergency Medicine}, volume = {22}, number = {10}, year = {2015}, month = {10/2015}, pages = {1187-1195}, doi = {10.1111/acem.12753}, author = {Yeo, Caitlin T and Colleen Davison and Tamas Ungi and M. Holden and Gabor Fichtinger and McGraw, Robert C} } @article {Holden2015a, title = {Objective Evaluation Of Sonographic Skill In Focussed Assessment With Sonography For Trauma Examinations}, volume = {10}, year = {2015}, month = {06/2015}, pages = {S79-S80}, address = {Barcelona, Spain}, doi = {10.1007/s11548-015-1213-2}, author = {M. Holden and Tamas Ungi and Conor McKaigney and Colin Bell and Louise Rang and Gabor Fichtinger} } @article {Soehl2015a, title = {Scalable Ultrasound Calibration Phantoms Made From LEGO{\textregistered} Bricks}, volume = {10}, year = {2015}, month = {06/2015}, pages = {S218-S219}, address = {Barcelona, Spain}, doi = {10.1007/s11548-015-1213-2}, author = {Marie Soehl and M. Holden and Andras Lasso and Gabor Fichtinger} } @article {Holden2014a, title = {Feasibility of Real-Time Workflow Segmentation for Tracked Needle Interventions}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {61}, number = {6}, year = {2014}, month = {06/2014}, pages = {1720-1728}, issn = {0018-9294}, doi = {10.1109/TBME.2014.2301635}, author = {M. Holden and Tamas Ungi and Derek Sargent and McGraw, Robert C and Elvis C. S. Chen and Sugantha Ganapathy and T. M. Peters and Gabor Fichtinger} } @article {Holden2014b, title = {Linear Object Registration: A Registration Algorithm using Points, Lines, and Planes}, year = {2014}, month = {03/2014}, address = {Toronto, Canada}, author = {M. Holden and Gabor Fichtinger} } @mastersthesis {Holden2014d, title = {Linear Object Registration for Image-Guided Interventions}, volume = {Masters}, year = {2014}, month = {08/2014}, school = {Queen{\textquoteright}s University}, type = {Masters}, address = {Kingston, ON}, url = {https://qspace.library.queensu.ca/handle/1974/12373}, author = {M. Holden} } @conference {Holden2014c, title = {Linear Object Registration of Interventional Tools}, booktitle = {Workshop on Augmented Environments for Computer-Assisted Interventions}, volume = {LNCS 8678}, year = {2014}, month = {09/2014}, pages = {118-127}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {Linear Object Registration of Interventional Tools}, address = {Boston, MA}, abstract = {

PURPOSE: Point-set registration for interventional tools requires well-defined points to be present on these tools. In this work, an algorithm is proposed which uses planes, lines, and points for registration when point-set registration is not feasible. METHODS: The proposed algorithm matches points, lines, and planes in each coordinate system, uses invariant features for initial registration, and optimizes the registration iteratively. For validation, simulated data with known ground-truth and real surgical tool registration data using point-set registration as ground-truth were created to evaluate the algorithm{\textquoteright}s accuracy. RESULTS: The proposed algorithm is equally as accurate as point-set registration, and the difference between the registrations is less than the noise in the tracking system. CONCLUSION: The proposed algorithm is a viable alternative when point-set registration cannot be performed.

}, keywords = {Coordinate transformations, Registration, Surgical navigation}, doi = {10.1007/978-3-319-10437-9_13}, url = {http://link.springer.com/chapter/10.1007/978-3-319-10437-9_13}, author = {M. Holden and Gabor Fichtinger} } @article {Keri2014a, title = {A novel technology for teaching ultrasound-guided lumbar puncture with Perk Tutor}, year = {2014}, month = {03/2014}, address = {Toronto, Ontario, March 23, 2014}, author = {Keri, Zsuzsanna and Devin Sydor and Tamas Ungi and M. Holden and McGraw, Robert C and Daniel P. Borschneck and Melanie Jaeger and Gabor Fichtinger} } @article {Ungi2014b, title = {Tracked ultrasonography snapshots enhance needle guidance for percutaneous renal access: a pilot study}, journal = {J Endourol}, volume = {28}, number = {9}, year = {2014}, month = {05/2014}, pages = {1040-5}, author = {Tamas Ungi and Darren Beiko and Michael Fuoco and Franklin King and M. Holden and Gabor Fichtinger and Robert Siemens} } @article {Ungi2014c, title = {Tracked ultrasound snapshots improve the performance of novices in simulated percutaneous nephrostomy}, year = {2014}, month = {03/2014}, address = {Toronto, Ontario, March 23, 2014}, author = {Gabor Fichtinger and Robert Siemens and M. Holden and Tamas Ungi and Darren Beiko and Michael Fuoco and Franklin King} } @article {Holden2013a, title = {Real-Time Workflow Segmentation for Needle-Based Interventions}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {M. Holden and Tamas Ungi and Sargent, D. and McGraw, Robert C and Elvis C. S. Chen and Sugantha Ganapathy and T. M. Peters and Gabor Fichtinger} } @conference {Holden2012a, title = {Surgical Motion Characterization in Simulated Needle-Insertion Procedures}, booktitle = {SPIE Medical Imaging 2012}, year = {2012}, month = {02/2012}, pages = {8316-31}, address = {San Diego, California, USA}, keywords = {Lumbar Puncture, Markov Models, Needle trajectories, Simulated procedures, Task segmentation}, doi = {http://dx.doi.org/10.1117/12.911003}, author = {M. Holden and Tamas Ungi and Sargent, D. and McGraw, Robert C and Gabor Fichtinger} }