@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 {Choueib2019b, title = {Assessment of immersive medical virtual reality visualization using 3D Slicer}, booktitle = {17th Annual Imaging Network Ontario Symposium (ImNO)}, year = {2019}, month = {03/2019}, address = {London, Ontario}, keywords = {3D Slicer, Planning, SlicerVR, training, Virtual Reality}, author = {Saleh Choueib and Csaba Pinter and Andras Lasso and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and Gabor Fichtinger} } @conference {Choueib2019a, title = {Evaluation of 3D Slicer as a medical virtual reality visualization platform}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, number = {38}, year = {2019}, month = {03/2019}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego, California }, keywords = {3D Slicer, Planning, SlicerVR, training, Virtual Reality}, author = {Saleh Choueib and Csaba Pinter and Andras Lasso and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and Gabor Fichtinger} } @article {Pinter2019, title = {Polymorph Segmentation Representation for Medical Image Computing}, journal = {Computer Methods and Programs in Biomedicine}, volume = {171}, year = {2019}, month = {02/2019}, pages = {19-26}, abstract = {

Background and Objective: Segmentation is a ubiquitous operation in medical image computing. Various data representations can describe segmentation results, such as labelmap volumes or surface models. Conversions between them are often required, which typically include complex data processing steps. We identified four challenges related to managing multiple representations: conversion method selection, data provenance, data consistency, and coherence of in-memory objects. Methods: A complex data container preserves identity and provenance of the contained representations and ensures data coherence. Conversions are executed automatically on-demand. A graph containing the implemented conversion algorithms determines each execution, ensuring consistency between various representations. The design and implementation of a software library are proposed, in order to provide a readily usable software tool to manage segmentation data in multiple data representations. A low-level core library called PolySeg implemented in The Visualization Toolkit (VTK) manages the data objects and conversions. It is used by a high-level application layer, which has been implemented in the medical image visualization and analysis platform 3D Slicer. The application layer provides advanced visualization, transformation, interoperability, and other functions. Results: The core conversion algorithms comprising the graph were validated. Several applications were implemented based on the library, demonstrating advantages in terms of usability and ease of software development in each case. The Segment Editor application provides fast, comprehensive, and easy-to-use manual and semi-automatic segmentation workflows. Clinical applications for gel dosimetry, external beam planning, and MRI-ultrasound image fusion in brachytherapy were rapidly prototyped resulting robust applications that are already in use in clinical research. The conversion algorithms were found to be accurate and reliable using these applications. Conclusions:\  A generic software library has been designed and developed for automatic management of multiple data formats in segmentation tasks. It enhances both user and developer experience, enabling fast and convenient manual workflows and quicker and more robust software prototyping. The software{\textquoteright}s BSD-style open-source license allows complete freedom of use of the library.

}, keywords = {3D Slicer, DICOM, Open-source, segmentation, Software library, Voxelization}, doi = {https://doi.org/10.1016/j.cmpb.2019.02.011}, author = {Csaba Pinter and Andras Lasso 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} } @article {Seifabadi2013a, title = {Real-Time 3D Needle Shape Tracking Using Fiber Bragg Grating Sensors for Prostate Percutaneous Interventions}, year = {2013}, address = {Crystal City, VA, USA}, abstract = {

During prostate needle insertion, the gland and therefore targets move resulting in targeting inaccuracy. To compensate for this error, in a previous study we proposed robotic bevel-tip needle steering under live MRI. One important drawback is that tracking a flexible (20G) needle under real-time MRI is inaccurate due to the needle artifact and the image update frequency is relatively slow (\>200 ms). As an alternative solution, we embedded some FBG strain sensors along the needle shaft at certain locations and reconstruct the needle shape from this information. Compared to the previous study on this topic by others, the following development have been done: 1) this study is extended to needle of higher gauge (thinner), 2) we guarantee below 0.5 mm accuracy at the needle tip for all insertion depth, 3) we extended it to bevel-tip needles (through modelling and mathematical formulation), 4) we visualized the 3D shape of the needle in 3D Slicer which is a commonly used planning and navigation software for prostate interventions, 5) our needle tracking is real-time.

}, keywords = {3D Slicer, MRI, needle tracking, real-time}, author = {Reza Seifabadi and Esteban Escobar Gomez and Fereshteh Aalamifar and Gabor Fichtinger and Iulian Iordachita} } @article {Pinter2012b, title = {SlicerRT - Radiation therapy research toolkit for 3D Slicer}, volume = {39}, year = {2012}, month = {10/2012}, pages = {6332/7}, abstract = {

Purpose: Interest in adaptive radiation therapy research is constantly growing, but software tools available for researchers are mostly either expensive, closed proprietary applications or free open-source packages with limited scope, extensibility, reliability, or user support. To address these limitations, we propose SlicerRT, a customizable, free, open-source radiation therapy research toolkit. SlicerRT aspires to be an open-source toolkit for RT research, providing fast computations, convenient workflows for researchers, and a general image-guided therapy infrastructure to assist clinical translation of experimental therapeutic approaches. It is a medium into which RT researchers can integrate their methods and algorithms, and conduct comparative testing. Methods: SlicerRT was implemented as an extension for the widely used 3D Slicer medical image visualization and analysis application platform. SlicerRT provides functionality specifically designed for radiation therapy research, in addition to the powerful tools that 3D Slicer offers for visualization, registration, segmentation, and data management. The feature set of SlicerRT was defined through consensus discussions with a large pool of RT researchers, including both radiation oncologists and medical physicists. The development processes used were similar to those of 3D Slicer to ensure software quality. Standardized mechanisms of 3D Slicer were applied for documentation, distribution, and user support. The testing and validation environment was configured to automatically launch a regression test upon each software change and to perform comparison with ground truth results provided by other RT applications. Results: Modules have been created for importing and loading DICOM-RT data, computing and displaying dose volume histograms, creating accumulated dose volumes, comparing dose volumes, visualizing isodose lines and surfaces. The effectiveness of using 3D Slicer with the proposed SlicerRT extension for radiation therapy research was demonstrated on multiple use cases.

}, keywords = {3D Slicer, DICOM-RT, dose comparison, dose volume histogram, Radiation therapy}, doi = {http://dx.doi.org/10.1118/1.4754659}, author = {Csaba Pinter and Andras Lasso and An Wang and David Jaffray and Gabor Fichtinger} }