@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 {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} }