Profile – Perk Lab

Jacob Lamframboise

Undergraduate Student

School of Computing

Queen's University

Jacob started with us as high school intern, and later was as undergraduate researcher at the Perk Lab. Jacob is studying Computing with a Focus in Artificial Intelligence and a Minor in Mathematics in the School of Computing. he was also the lab's Outreach Manager.

Barr, Keiran; Laframboise, Jacob; Ungi, Tamas; Hookey, Lawrence; Fichtinger, Gabor

Automated segmentation of computed tomography colonography images using a 3D U-Net Conference

SPIE Medical Imaging, 2020.

Links | BibTeX

Laframboise, Jacob; Ungi, Tamas; Sunderland, Kyle R.; Zevin, Boris; Fichtinger, Gabor

Open source platform for automated collection of training data to support video-based feedback in surgical simulators Conference

SPIE Medical Imaging, SPIE, Houston, United States, 2020.

Abstract | BibTeX

@conference{Laframboise2020a,

title = {Open source platform for automated collection of training data to support video-based feedback in surgical simulators},

author = {Jacob Laframboise and Tamas Ungi and Kyle R. Sunderland and Boris Zevin and Gabor Fichtinger},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {SPIE Medical Imaging},

publisher = {SPIE},

address = {Houston, United States},

abstract = {<p><strong>Purpose:</strong> 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. <strong>Methods:</strong> 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. <strong>Results:</strong> 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’s predictions can be weighted over several frames with a custom Slicer module to improve accuracy. <strong>Conclusion:</strong> 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.</p>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Laframboise, Jacob; Ungi, Tamas; Lasso, Andras; Asselin, Mark; Holden, M.; Tan, Pearl; Hookey, Lawrence; Fichtinger, Gabor

Analyzing the curvature of the colon in different patient positions Conference

SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10951, San Diego, California, 2019.

Links | BibTeX

Laframboise, Jacob; Ungi, Tamas; Lasso, Andras; Asselin, Mark; Holden, M.; Tan, Pearl; Hookey, Lawrence; Fichtinger, Gabor

Quantifying the effect of patient position on the curvature of colons Conference

17th Annual Imaging Network Ontario Symposium (ImNO), Imaging Network Ontario (ImNO), London, Ontario, 2019.

Links | BibTeX