@conference {KBarr2020, title = {Automated segmentation of computed tomography colonography images using a 3D U-Net}, booktitle = {SPIE Medical Imaging}, year = {2020}, month = {03/2020}, doi = {https://doi.org/10.1117/12.2549749}, author = {Keiran Barr and Jacob Laframboise and Tamas Ungi and Lawrence Hookey and Gabor Fichtinger} } @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} } @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} }