Jacob Lamframboise
Undergraduate Student
School of Computing
Queen’s University
Member from 2018 to present
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.
@conference{KBarr2020,
title = {Automated segmentation of computed tomography colonography images using a 3D U-Net},
author = {Keiran Barr and Jacob Laframboise and Tamas Ungi and Lawrence Hookey and Gabor Fichtinger},
doi = {https://doi.org/10.1117/12.2549749},
year = {2020},
date = {2020-03-01},
urldate = {2020-03-01},
booktitle = {SPIE Medical Imaging},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@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}
}
<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>
Laframboise, Jacob; Ungi, Tamas; Sunderland, Kyle; Zevin, Boris; Fichtinger, Gabor
Open-source platform for automated collection of training data to support video-based feedback in surgical simulators Journal Article
In: vol. 11315, pp. 317-323, 2020.
@article{fichtinger2020p,
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 Sunderland and Boris Zevin and Gabor Fichtinger},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11315/1131517/Open-source-platform-for-automated-collection-of-training-data-to/10.1117/12.2549878.short},
year = {2020},
date = {2020-01-01},
volume = {11315},
pages = {317-323},
publisher = {SPIE},
abstract = {Purpose
Surgical training could be improved by automatic detection of workflow steps, and similar applications of image processing. A platform to collect and organize tracking and video data would enable rapid development of image processing solutions for surgical training. The purpose of this research is to demonstrate 3D Slicer / PLUS Toolkit as a platform for automatic labelled data collection and model deployment.
Methods
We use PLUS and 3D Slicer to collect a labelled dataset of tools interacting with tissues in simulated hernia repair, comprised of optical tracking data and video data from a camera. To demonstrate the platform, we train a neural network on this data to automatically identify tissues, and 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 of 128,548 labelled frames, with 98.5% correctly …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Surgical training could be improved by automatic detection of workflow steps, and similar applications of image processing. A platform to collect and organize tracking and video data would enable rapid development of image processing solutions for surgical training. The purpose of this research is to demonstrate 3D Slicer / PLUS Toolkit as a platform for automatic labelled data collection and model deployment.
Methods
We use PLUS and 3D Slicer to collect a labelled dataset of tools interacting with tissues in simulated hernia repair, comprised of optical tracking data and video data from a camera. To demonstrate the platform, we train a neural network on this data to automatically identify tissues, and 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 of 128,548 labelled frames, with 98.5% correctly …
Surgical training could be improved by automatic detection of workflow steps, and similar applications of image processing. A platform to collect and organize tracking and video data would enable rapid development of image processing solutions for surgical training. The purpose of this research is to demonstrate 3D Slicer / PLUS Toolkit as a platform for automatic labelled data collection and model deployment.
Methods
We use PLUS and 3D Slicer to collect a labelled dataset of tools interacting with tissues in simulated hernia repair, comprised of optical tracking data and video data from a camera. To demonstrate the platform, we train a neural network on this data to automatically identify tissues, and 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 of 128,548 labelled frames, with 98.5% correctly …
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.
@conference{Laframboise2019a,
title = {Analyzing the curvature of the colon in different patient positions},
author = {Jacob Laframboise and Tamas Ungi and Andras Lasso and Mark Asselin and M. Holden and Pearl Tan and Lawrence Hookey and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Laframboise2019a.pdf},
year = {2019},
date = {2019-03-01},
urldate = {2019-03-01},
booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling},
volume = {10951},
address = {San Diego, California},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@conference{Laframboise2019b,
title = {Quantifying the effect of patient position on the curvature of colons},
author = {Jacob Laframboise and Tamas Ungi and Andras Lasso and Mark Asselin and M. Holden and Pearl Tan and Lawrence Hookey and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Laframboise2019b.pdf},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {17th Annual Imaging Network Ontario Symposium (ImNO)},
publisher = {Imaging Network Ontario (ImNO)},
address = {London, Ontario},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}