@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 {Kapur2016, title = {Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience}, journal = {Medical Image Analysis}, volume = {33}, year = {2016}, month = {10/2016}, pages = {176-180}, abstract = {

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools{\textemdash}VTK, ITK, CMake, CDash, DCMTK{\textemdash}were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ({\textquotedblleft}Open Science{\textquotedblright}); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

}, keywords = {3D Slicer, Hackathon, Medical image computing, NA-MIC, Open access, Open science, Open source, Project week, Reproducible research}, doi = {http://doi.org/10.1016/j.media.2016.06.035}, author = {Tina Kapur and Steve Pieper and Andras Lasso and Tamas Ungi and Csaba Pinter and Gabor Fichtinger and Ron Kikinis} }