@article {Klosa2022b, title = {Identifying tissues for task recognition in training of open inguinal hernia repairs}, year = {2022}, month = {02/2022}, author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger} } @conference {Klosa2022a, title = {Tissue segmentation for workflow recognition in open inguinal hernia repair training}, booktitle = {SPIE Medical Imaging}, year = {2022}, month = {02/2022}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego}, abstract = {PURPOSE: As medical education adopts a competency-based training method, experts are spending substantial amounts of time instructing and assessing trainees{\textquoteright} competence. In this study, we look to develop a computer-assisted training platform that can provide instruction and assessment of open inguinal hernia repairs without needing an expert observer. We recognize workflow tasks based on the tool-tissue interactions, suggesting that we first need a method to identify tissues. This study aims to train a neural network in identifying tissues in a low-cost phantom as we work towards identifying the tool-tissue interactions needed for task recognition. METHODS: Eight simulated tissues were segmented throughout five videos from experienced surgeons who performed open inguinal hernia repairs on phantoms. A U-Net was trained using leave-one-user-out cross validation. The average F-score, false positive rate and false negative rate were calculated for each tissue to evaluate the U-Net{\textquoteright}s performance. RESULTS: Higher F-scores and lower false negative and positive rates were recorded for the skin, hernia sac, spermatic cord, and nerves, while slightly lower metrics were recorded for the subcutaneous tissue, Scarpa{\textquoteright}s fascia, external oblique aponeurosis and superficial epigastric vessels. CONCLUSION: The U-Net performed better in recognizing tissues that were relatively larger in size and more prevalent, while struggling to recognize smaller tissues only briefly visible. Since workflow recognition does not require perfect segmentation, we believe our U-Net is sufficient in recognizing the tissues of an inguinal hernia repair phantom. Future studies will explore combining our segmentation U-Net with tool detection as we work towards workflow recognition.}, keywords = {open inguinal hernia repair, segmentation, surgical training, U-Net, workflow recognition}, author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger} } @article {Poole2021, title = {Training in soft tissue resection using real-time visual computer navigation feedback from the Surgery Tutor: A randomized controlled trial}, journal = {Surgery}, year = {2021}, month = {12/2021}, abstract = {

Background In competency-based medical education, surgery trainees are often required to learn procedural skills in a simulated setting before proceeding to the clinical environment. The Surgery Tutor computer navigation platform allows for real-time proctor-less assessment of open soft tissue resection skills; however, the use of this platform as an aid in acquisition of procedural skills is yet to be explored. Methods In this prospective randomized controlled trial, 20 final year medical students were randomized to receive either training with real-time computer navigation feedback (Intervention, n\ = 10) or simulation training without navigation feedback (Control, n\ = 10) during resection of simulated non-palpable soft tissue tumors. Real-time computer navigation feedback allowed participants to visualize the position of their scalpel relative to the tumor. Computer navigation feedback was removed for postintervention assessment. Primary outcome was positive margin rate. Secondary outcomes were procedure time, mass of tissue excised, number of scalpel motions, and distance traveled by the scalpel. Results Training with real-time computer navigation resulted in a significantly lower positive margin rate as compared to training without navigation feedback (0\% vs 40\%, P\ = .025). All other performance metrics were not significantly different between the 2 groups. Participants in the intervention group displayed significant improvement in positive margin rate from baseline to final assessment (80\% vs 0\%, P\ \< .01), whereas participants in the Control group did not. Conclusion Real-time visual computer navigation feedback from the Surgery Tutor resulted in superior acquisition of procedural skills as compared to training without navigation feedback.

}, issn = {0039-6060}, doi = {https://doi.org/10.1016/j.surg.2021.11.037}, url = {https://www.sciencedirect.com/science/article/pii/S0039606021011855}, author = {Meredith Poole and Tamas Ungi and Gabor Fichtinger and Boris Zevin} } @article {Laframboise2020a, title = {Open source platform for automated collection of training data to support video-based feedback in surgical simulators}, year = {2020}, publisher = {SPIE}, address = {Houston, United States}, abstract = {

Purpose: Surgical training could be improved by automatic detection of workflow steps. A platform to collect and organize tracking and video data would enable rapid development of deep learning solutions for surgical training. The purpose of this research is to demonstrate 3D Slicer / PLUS Toolkit as a platform for video annotation by identifying and annotating tools interacting with tissues in simulated hernia repair. Methods: Tracking data from an optical tracker and video data from a camera are collected by PLUS and 3D Slicer. To demonstrate the platform in use, we identify tissues during a surgical procedure using a neural network. The tracking data is used to identify what tool is in use. The solution is deployed with a custom Slicer module. Results: This platform allowed the collection and storage of enough tracked video data for training a convolutional neural network (CNN) to detect interactions with tissues and tools. The CNN was trained on this data and applied to new data with a testing accuracy of 98\%. The model{\textquoteright}s predictions can be weighted over several frames with a custom Slicer module to improve accuracy. Conclusion: We found the 3D Slicer and PLUS Toolkit platform to be a viable platform for training and deploying a solution that combines automatic video processing and optical tool tracking. We designed a proof of concept model to identify tissues with a trained CNN in real time along with tracking of surgical tools.

}, keywords = {3D Slicer, AI, annotation, collection, data, deep learning, PLUS, surgical training, video}, author = {Jacob Laframboise and Tamas Ungi and Kyle R. Sunderland and Boris Zevin 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} } @article {Yeo2017, title = {Utility of 3D Reconstruction of 2D Liver Computed Tomography/Magnetic Resonance Images as a Surgical Planning Tool for Residents in Liver Resection Surgery}, journal = {Journal of Surgical Education}, volume = {75}, number = {3}, year = {2018}, month = {08/2017}, pages = {792-797}, abstract = {

Objective

A fundamental aspect of surgical planning in liver resections is the identification of key vessel tributaries to preserve healthy liver tissue while fully resecting the tumor(s). Current surgical planning relies primarily on the surgeon{\textquoteright}s ability to mentally reconstruct 2D computed tomography/magnetic resonance (CT/MR) images into 3D and plan resection margins. This creates significant cognitive load, especially for trainees, as it relies on image interpretation, anatomical and surgical knowledge, experience, and spatial sense. The purpose of this study is to determine if 3D reconstruction of preoperative CT/MR images will assist resident-level trainees in making appropriate operative plans for liver resection surgery.

Design

Ten preoperative patient CT/MR images were selected. Images were case-matched, 5 to 2D planning and 5 to 3D planning. Images from the 3D group were segmented to create interactive digital models that the resident can manipulate to view the tumor(s) in relation to landmark hepatic structures. Residents were asked to evaluate the images and devise a surgical resection plan for each image. The resident alternated between 2D and 3D planning, in a randomly generated order. The primary outcome was the accuracy of resident{\textquoteright}s plan compared to expert opinion. Time to devise each surgical plan was the secondary outcome. Residents completed a prestudy and poststudy questionnaire regarding their experience with liver surgery and the 3D planning software.

Setting and Participants

Senior level surgical residents from the Queen{\textquoteright}s University General Surgery residency program were recruited to participate.

Results

A total of 14 residents participated in the study. The median correct response rate was 2 of 5 (40\%; range: 0-4) for the 2D group, and 3 of 5 (60\%; range: 1-5) for the 3D group (p \< 0.01). The average time to complete each plan was 156 {\textpm} 107 seconds for the 2D group, and 84 {\textpm} 73 seconds for the 3D group (p \< 0.01). A total 13 of 14 residents found the 3D model easier to use than the 2D. Most residents noticed a difference between the 2 modalities and found that the 3D model improved their confidence with the surgical plan proposed.

Conclusions

The results of this study show that 3D reconstruction for liver surgery planning increases accuracy of resident surgical planning and decreases amount of time required. 3D reconstruction would be a useful model for improving trainee understanding of liver anatomy and surgical resection, and would serve as an adjunct to current 2D planning methods. This has the potential to be developed into a module for teaching liver surgery in a competency-based medical curriculum.

}, doi = {http://dx.doi.org/10.1016/j.jsurg.2017.07.031}, author = {Yeo, Caitlin T and MacDonald, Andrew and Tamas Ungi and Andras Lasso and Diederick Jalink and Boris Zevin and Gabor Fichtinger and Nanji, Sulaiman} } @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} }