@article {Ungi2020, title = {Automatic spine ultrasound segmentation for scoliosis visualization and measurement}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {67}, number = {11}, year = {2020}, month = {03/2020}, pages = {3234 - 3241}, abstract = {

Objective: Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging. Methods: We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement. Results: As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2\% decrease), and precision from 0.31 to 0.27 (3.7\% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6\% decrease), and precision from 0.10 to 0.06 (4.5\% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2{\textdegree} compared to X-ray. Conclusion: automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. Significance: Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.

}, doi = {10.1109/TBME.2020.2980540}, url = {https://ieeexplore.ieee.org/document/9034149}, author = {Tamas Ungi and Hastings Greer and Kyle R. Sunderland and Victoria Wu and Zachary M C Baum and Christopher Schlenger and Matthew Oetgen and Kevin Cleary and Stephen Aylward and Gabor Fichtinger} } @article {Baum2019b, title = {Assessment of intraoperative neurosurgical planning with the Microsoft HoloLens}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, author = {Zachary M C Baum and Sarah Ryan and Emily Rae and Andras Lasso and Tamas Ungi and Ron Levy and Gabor Fichtinger} } @article {Baum2020, title = {Augmented reality training platform for neurosurgical burr hole localization}, journal = {Journal of Medical Robotics Research}, volume = {4}, number = {3-4}, year = {2019}, month = {01/2020}, pages = {1942001-1 - 1942001-13}, type = {Special Issue}, abstract = {

Augmented reality (AR) is used in neurosurgery to visualize lesions and plan procedures pre-operatively and intra-operatively, though its use has not been widely adopted in simulation-based neurosurgical training for the same tasks. This work defines metrics to determine performance in drill position and angle identification for neurosurgical training. The metrics were validated intra-operatively and in a simulated training environment, demonstrating that trainees identify drill position and angle faster and more accurately with AR compared to standard techniques. Training using AR and the proposed metrics stands to add value to neurosurgical curricula development.

}, keywords = {augmented reality, competency-based medical education, holography, neurosurgery, surgical planning, visualization}, doi = {https://doi.org/10.1142/S2424905X19420017}, author = {Zachary M C Baum and Andras Lasso and Sarah Ryan and Tamas Ungi and Emily Rae and Boris Zevin and Ron Levy and Gabor Fichtinger} } @mastersthesis {Baum2019c, title = {AUGMENTED REALITY TRAINING PLATFORM FOR PLACEMENT OF NEUROSURGICAL BURR HOLES}, volume = {Masters}, year = {2019}, month = {04/2019}, school = {Queen{\textquoteright}s University}, type = {Masters}, address = {Kingston, ON}, abstract = {

Augmented reality has been used in neurosurgery to aid in the visualization of lesions, though it has not been widely adopted for simulation-based neurosurgical training. This work aims to determine if augmented reality can improve identification of drill location and drill angle for neurosurgical procedures and to define objective metrics for assessing trainee performance.
An augmented reality visualization system was developed using the Microsoft HoloLens. Trainee performance metrics were defined and validated intra-operatively in fifteen neurosurgical cases by attending neurosurgeons and trainees. Trainee performance in localization of drill location and angle tasks was assessed in a simulated training with augmented reality visualization and compared with two other visualization methods.
The proposed metrics allowed us to significantly differentiate levels of competence between attending neurosurgeons and trainees in identification of drill location with (p = 0.011) and without (p = 0.001) the HoloLens and drill angle with (p = 0.032) the HoloLens. Augmented reality visualization significantly improved trainee performance in localization of drill location (p \< 0.001 and p = 0.008) and angle (p \< 0.001 and p \< 0.001) in comparison to two other visualization methods. Trainees rated augmented reality visualization equally or more helpful compared to the two other visualization methods.
Trainee performance assessment with augmented reality visualization and the proposed performance metrics stands to add practical value to neurosurgical training curricula. This work represents a necessary step in curriculum development in neurosurgical training for the task of drill location and angle localization in a variety of neurosurgical procedures.

}, url = {http://hdl.handle.net/1974/26123}, author = {Zachary M C Baum} } @conference {Perrin2019a, title = {Reproducibility of freehand calibrations for ultrasound-guided needle navigation}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, address = {San Diego, California}, author = {Sydney Perrin and Zachary M C Baum and Mark Asselin and Underwood, Grace and Saleh Choueib and H. Lia and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @conference {Baum2019a, title = {Step-wise identification of ultrasound-visible anatomical landmarks for 3D visualization of scoliotic spine}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, author = {Zachary M C Baum and Ben Church and Andras Lasso and Tamas Ungi and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger} } @article {Lia2019a, title = {Usability and accuracy of an electromagnetically tracked partial nephrectomy navigation system}, year = {2019}, month = {03/2019}, author = {H. Lia and Zachary M C Baum and Thomas Vaughan and Tamas Ungi and McGregor, T. and Gabor Fichtinger} } @conference {Wiercigroch2019, title = {Validation of a low-cost adjustable, handheld needle guide for spine interventions}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, keywords = {3D printing, MR-guided, needle guidance, spinal injection}, author = {Wiercigroch, Julia and Zachary M C Baum and Tamas Ungi and Jan Fritz 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 {Pinter2018a, title = {Real-time transverse process delineation in tracked ultrasound for scoliosis measurement}, year = {2018}, month = {03/2018}, address = {Toronto, Canada}, author = {Csaba Pinter and Bryan Travers and Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Gabor Fichtinger} } @conference {Pinter2018, title = {Real-time transverse process detection in ultrasound}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2018}, month = {03/2018}, address = {Houston, Texas}, author = {Csaba Pinter and Bryan Travers and Zachary M C Baum and Shahrokh Kamali and Tamas Ungi and Andras Lasso and Ben Church and Gabor Fichtinger} } @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 {Baum2018b, title = {Ultrasound-based vertebral landmark localization using deformable spine models}, year = {2018}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Christopher Schlenger and Gabor Fichtinger} } @conference {Baum2018a, title = {Visual aid for identifying vertebral landmarks in ultrasound}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10576}, year = {2018}, month = {03/2018}, pages = {105760Z}, address = {Houston, Texas}, doi = {10.1117/12.2292820}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Christopher Schlenger and Gabor Fichtinger} } @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 {Ilina2017b, title = {Creating patient-specific anatomical models from highly elastic materials using 3D-printed molds}, year = {2017}, address = {London, Canada}, keywords = {cardiac, heart, heart valve models, heart valves, molds, pediatric, Phantoms}, author = {Anna Ilina and Andras Lasso and MatthewA Jolley and Brittany Wohler and Alexander V. Nguyen and Adam B. Scanlan and Zachary M C Baum and FrancisX McGowan and Gabor Fichtinger} } @article {Baum2017b, title = {Evaluation of a mobile, real-time, tracked augmented reality display for surgical navigation}, year = {2017}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Canada}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @conference {House2017, title = {Evaluation of the Intel RealSense SR300 camera for image-guided interventions and application in vertebral level localization}, booktitle = {SPIE Medical Imaging }, year = {2017}, author = {Rachael House and Andras Lasso and Vinyas Harish and Zachary M C Baum and Gabor Fichtinger} } @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 {Ilina2017a, title = {Patient-specific pediatric silicone heart valve models based on 3D ultrasound}, booktitle = {SPIE Medical Imaging 2017}, volume = {10135}, year = {2017}, month = {03/2017}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {Orlando, FL, United States, Feb. 16, 2017}, abstract = {

PURPOSE: Patient-specific heart and valve models have shown promise as training and planning tools for heart surgery, but physically realistic valve models remain elusive. Available proprietary, simulation-focused heart valve models are generic adult mitral valves and do not allow for patient-specific modeling as may be needed for rare diseases such as congenitally abnormal valves. We propose creating silicone valve models from a 3D-printed plastic mold as a solution that can be adapted to any individual patient and heart valve at a fraction of the cost of direct 3D-printing using soft materials.

METHODS: Leaflets of a pediatric mitral valve, a tricuspid valve in a patient with hypoplastic left heart syndrome, and a complete atrioventricular canal valve were segmented from ultrasound images. A custom software was developed to automatically generate molds for each valve based on the segmentation. These molds were 3D-printed and used to make silicone valve models. The models were designed with cylindrical rims of different sizes surrounding the leaflets, to show the outline of the valve and add rigidity. Pediatric cardiac surgeons practiced suturing on the models and evaluated them for use as surgical planning and training tools.

RESULTS: Five out of six surgeons reported that the valve models would be very useful as training tools for cardiac surgery. In this first iteration of valve models, leaflets were felt to be unrealistically thick or stiff compared to real pediatric leaflets. A thin tube rim was preferred for valve flexibility.

CONCLUSION: The valve models were well received and considered to be valuable and accessible tools for heart valve surgery training. Further improvements will be made based on surgeons{\textquoteright} feedback.

Keywords: surgery, training, heart valve models, pediatric, patient-specific, 3D-printing, congenital heart disease, mitral valve, tricuspid valve, complete atrioventricular canal defect, ultrasound

}, author = {Anna Ilina and Andras Lasso and MatthewA Jolley and Brittany Wohler and Alexander V. Nguyen and Adam B. Scanlan and Zachary M C Baum and FrancisX McGowan and Gabor Fichtinger} } @article {Bibic2017a, title = {PLUS Model Catalog: A library of 3D-printable models}, year = {2017}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Canada}, author = {Eden Bibic and Zachary M C Baum and Vinyas Harish and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @article {Baum2017c, title = {Real-Time, Tracked, Mobile Augmented Reality Display for Surgical Navigation: Usability Study on Simulated Patients}, year = {2017}, publisher = {National Center for Image Guided Therapy}, address = {Bethesda, USA}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @conference {Underwood2017, title = {Skull registration for prone patient position using tracked ultrasound}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, author = {Underwood, Grace and Tamas Ungi and Zachary M C Baum and Andras Lasso and Gernot Kronreif and Gabor Fichtinger} } @conference {Baum2017a, title = {Usability of a real-time tracked augmented reality display system in musculoskeletal injections}, booktitle = {SPIE Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2017}, month = {03/2017}, doi = {10.1117/12.2255897}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Gabor Fichtinger} } @conference {Harish2016a, title = {Measurement of electromagnetic tracking error in a navigated breast surgery setup}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego, CA, United States, Feb. 29, 2016}, abstract = {

The measurement of tracking error is crucial to ensure the safety and feasibility of electromagnetically tracked, image-guided procedures. Measurement should occur in a clinical environment because electromagnetic field distortion depends on positioning relative to the field generator and metal objects. However, we could not find an accessible and open-source system for calibration, error measurement, and visualization. We developed such a system and tested it in a navigated breast surgery setup.A pointer tool was designed for concurrent electromagnetic and optical tracking. Software modules were developed for automatic calibration of the measurement system, real-time error visualization, and analysis. The system was taken to an operating room to test for field distortion in a navigated breast surgery setup. Positional and rotational electromagnetic tracking errors were then calculated using optical tracking as a ground truth. Our system is quick to set up and can be rapidly deployed. The process from calibration to visualization also only takes a few minutes. Field distortion was measured in the presence of various surgical equipment. Positional and rotational error in a clean field was approximately 0.90 mm and 0.31{\textdegree}. The presence of a surgical table, an electrosurgical cautery, and anesthesia machine increased the error by up to a few tenths of a millimetre and tenth of a degree.In a navigated breast surgery setup, measurement and visualization of tracking error defines a safe working area in the presence of surgical equipment. Our system is available as an extension for the open-source 3D Slicer platform.

}, author = {Vinyas Harish and A Baksh and Tamas Ungi and Andras Lasso and Zachary M C Baum and G Gauvin and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Harish2016c, title = {Monitoring electromagnetic tracking error in computer-navigated breast cancer surgery}, year = {2016}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, keywords = {Breast cancer, EM Tracking, Lumpectomy}, author = {Vinyas Harish and A Baksh and Tamas Ungi and Andras Lasso and Zachary M C Baum and G Gauvin and C. Jay Engel and John Rudan and Gabor Fichtinger} } @article {Baum2016b, title = {Real-time self-calibration of a handheld augmented reality overlay system}, year = {2016}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, author = {Zachary M C Baum and Andras Lasso and Tamas Ungi and Gabor Fichtinger} } @conference {Baum2016a, title = {Real-time self-calibration of a tracked augmented reality display}, booktitle = {SPIE Medical Imaging 2016}, volume = {9786}, year = {2016}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego, CA, United States, Feb. 29, 2016}, abstract = {

PURPOSE: Augmented reality systems have been proposed for image-guided needle interventions but they have not become widely used in clinical practice due to restrictions such as limited portability, low display refresh rates, and tedious calibration procedures. We propose a handheld tablet-based self-calibrating image overlay system.

METHODS: A modular handheld augmented reality viewbox was constructed from a tablet computer and a semi-transparent mirror. A consistent and precise self-calibration method, without the use of any temporary markers, was designed to achieve an accurate calibration of the system. Markers attached to the viewbox and patient are simultaneously tracked using an optical pose tracker to report the position of the patient with respect to a displayed image plane that is visualized in real-time. The software was built using the open-source 3D Slicer application platform{\textquoteright}s SlicerIGT extension and the PLUS toolkit.

RESULTS: The accuracy of the image overlay with image-guided needle interventions yielded a mean absolute position error of 0.99 mm (95th percentile 1.93 mm) in-plane of the overlay and a mean absolute position error of 0.61 mm (95th percentile 1.19 mm) out-of-plane. This accuracy is clinically acceptable for tool guidance during various procedures, such as musculoskeletal injections.

CONCLUSION: A self-calibration method was developed and evaluated for a tracked augmented reality display. The results show potential for the use of handheld image overlays in clinical studies with image-guided needle interventions.\ 

}, author = {Zachary M C Baum and Andras Lasso and Tamas Ungi and Gabor Fichtinger} }