@article {Fedorov2020, title = {Quantitative Imaging Informatics for Cancer Research}, journal = {JCO Clinical Cancer Informatics}, volume = {4}, year = {2020}, pages = {444-453.}, doi = {https://doi.org/10. 1200/CCI.19.00165}, author = {Andriy Fedorov and Reinhard Beichel and Jayashree Kalpathy-Cramer and David Clunie and Michael Onken and J{\"o}rg Riesmeier and Christian Herz and Christian Bauer and Andrew Beers and Jean-ChristopheC. Fillion-Robin and Andras Lasso and Csaba Pinter and Steve Pieper and Marco Nolden and Klaus Maier-Hein and Markus D. Herrmann and Joel Saltz and Fred Prior and Fiona M. Fennessy and John Buatti and Ron Kikinis} } @article {Pinter2020, title = {SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality}, journal = {IEEE Transactions on Medical Robotics and Bionics}, volume = {2}, number = {2}, year = {2020}, month = {03/2020}, pages = {108-117}, abstract = {

Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.

}, doi = {10.1109/TMRB.2020.2983199}, author = {Csaba Pinter and Andras Lasso and Saleh Choueib and Mark Asselin and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and MatthewA Jolley and Gabor Fichtinger} } @article {Pinter2020a, title = {Using Fuzzy Logics to Determine Optimal Oversampling Factor for Voxelizing 3D Surfaces in Radiation Therapy}, journal = {Soft Computing}, year = {2020}, month = {06/2020}, doi = {10.1007/s00500-020-05126-w}, url = {https://link.springer.com/article/10.1007/s00500-020-05126-w}, author = {Csaba Pinter and Tim Olding and L. John Schreiner 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} } @article {Lasso2019, title = {Enhance medical software applications with immersive virtual reality experience}, year = {2019}, month = {10/2019}, address = {Toronto, ON, Canada}, author = {Andras Lasso and Csaba Pinter and Saleh Choueib and Tamas Ungi 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} } @conference {Pinter2019a, title = {SlicerVR for image-guided therapy planning in immersive virtual reality}, booktitle = {The 12th Hamlyn Symposium on Medical Robotics, 23-26 June 2019, Imperial College, London, UK}, year = {2019}, month = {06/2019}, pages = {91-92}, address = {London, UK}, author = {Csaba Pinter and Andras Lasso and Mark Asselin and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and Gabor Fichtinger} } @article {Ilina2018a, title = {3D Surface Scanning for Tumour Localization in Non-Melanoma Skin Cancer}, year = {2018}, month = {03/2018}, address = {Toronto, Canada}, author = {Anna Ilina and Csaba Pinter and Andras Lasso and Ingrid Lai and C.P. Joshi and Kevin Alexander and L. John Schreiner and Timothy Hanna and Gabor Fichtinger} } @article {Lasso2018, title = {Interaction with Volume-Rendered Three-Dimensional Echocardiographic Images in Virtual Reality}, journal = {J Am Soc Echocardiogr}, volume = {31}, number = {10}, year = {2018}, month = {10}, pages = {1158-60{\textendash}}, abstract = {

Three-dimensional (3D) imaging is increasingly important in echocardiography. However, viewing of 3D images on a flat, two-dimensional screen is a barrier to comprehension of latent information. There have been previous attempts to visualize the full 3D nature of the data, but they have not been widely adopted. For example, 3D printing offers realistic interaction but is time consuming, has limited means for the observer to move into or through the model, and is not yet practical for routine clinical use. Furthermore, the heart beats, and 3D printed models are static. Stereoscopic viewing on 2D screens (as at a movie theater) is possible but is expensive, may not provide an immersive experience, and does not have integrated 3D input devices (controllers).

}, author = {Andras Lasso and HannahH Nam and PatrickV Dinh and Csaba Pinter and Jean-ChristopheC. Fillion-Robin and Steve Pieper and Sankhesh Jhaveri and Jean-Baptiste Vimort and Ken Martin and Mark Asselin and FrancisX McGowan and Ron Kikinis and Gabor Fichtinger and MatthewA Jolley} } @article {Pinter2018a, title = {Real-time transverse process delineation in tracked ultrasound for scoliosis measurement}, year = {2018}, month = {03/2018}, address = {Toronto, Canada}, author = {Csaba Pinter and Bryan Travers and Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Gabor Fichtinger} } @conference {Pinter2018, title = {Real-time transverse process detection in ultrasound}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, year = {2018}, month = {03/2018}, address = {Houston, Texas}, author = {Csaba Pinter and Bryan Travers and Zachary M C Baum and Shahrokh Kamali and Tamas Ungi and Andras Lasso and Ben Church and Gabor Fichtinger} } @article {Alexander2018, title = {Streamlined open-source gel dosimetry analysis in 3D slicer}, journal = {Biomedical Physics \& Engineering Express}, volume = {4}, number = {4}, year = {2018}, month = {07/2018}, pages = {045041}, abstract = {

Three dimensional dosimetry is being used in an increasingly wide variety of clinical applications as more gel and radiochromic plastic dosimeters become available. However, accessible 3D dosimetry analysis tools have not kept pace. 3D dosimetry data analysis is time consuming and laborious, creating a barrier to entry for busy clinical environments. To help in the adoption of 3D dosimetry, we have produced a streamlined, open-source dosimetry analysis system by developing a custom extension in 3D Slicer, called the Gel Dosimetry Analysis slicelet, which enables rapid and accurate data analysis. To assist those interested in adopting 3D dosimetry in their clinic or those unfamiliar with what is involved in a 3D dosimeter experiment, we first present the workflow of a typical gel dosimetry experiment. This is followed by the results of experiments used to validate, step-wise, each component of our software. Overall, our software has made a full 3D gel dosimeter analysis roughly 20 times faster than previous analysis systems.

}, keywords = {3d dosimetry, gel dosimetry, Radiation therapy}, doi = {https://doi.org/10.1088/2057-1976/aad0cf}, url = {http://stacks.iop.org/2057-1976/4/i=4/a=045041}, author = {Kevin Alexander and Csaba Pinter and Gabor Fichtinger and Tim Olding and L. John Schreiner} } @article {Ilina2018b, title = {Target Definition with 3D Surface Scanning for Orthovoltage Radiation Therapy Planning}, year = {2018}, address = {Berlin, Germany}, author = {Anna Ilina and Csaba Pinter and Andras Lasso and Ingrid Lai and C.P. Joshi and Kevin Alexander and L. John Schreiner and Timothy Hanna and Gabor Fichtinger} } @article {Poulin2017b, title = {Validation of MRI to TRUS registration for HDR prostate brachytherapy}, journal = {Brachytherapy}, volume = {17}, number = {2}, year = {2018}, month = {2018/04}, pages = {283-290}, abstract = {

Purpose: The objective of this study was to develop and
validate an open-source module for MRI to transrectal ultrasound (TRUS)
registration to support tumor-targeted prostate brachytherapy.

Methods and Materials: In this study, fifteen patients with prostate
cancer lesions visible on multiparametric MRI were selected for the
validation. T2-weighted images with 1 mm isotropic voxel size and
diffusion weighted images were acquired on a 1.5T Siemens. Three
dimensional (3D) TRUS images with 0.5 mm slice thickness were acquired.
The investigated registration module was incorporated in the open-source
3D Slicer platform and can compute rigid and deformable transformations.
An extension of 3D Slicer, SlicerRT, allows import and export to DICOM-RT
formats. For validation, similarity indices, prostate volumes and
centroid positions were determined in addition to registration errors for
common 3D points identified by an experienced radiation oncologist.

Results: The average time to compute the registration was 35{\textpm}3 seconds.
For the deformable and rigid registration respectively, Dice similarity
coefficients were 0.93{\textpm}0.01 and 0.87{\textpm}0.05 while the 95\% Hausdorff
distance was 2.2{\textpm}0.3 and 4.2{\textpm}1.0 mm. MRI volumes obtained after the rigid
and deformable registration were not statistically different (p\>0.05)
from reference TRUS volumes. For the deformable and rigid registration
respectively, 3D distance error between centroid positions were 0.4{\textpm}0.1
and 2.1{\textpm}1.0 mm while registration errors between common points were
2.3{\textpm}1.1 and 3.5{\textpm}3.2 mm. Deformable registration was found significantly
better (p\<0.05) than rigid registration in all parameters.

Conclusions: An open-source MRI to TRUS registration platform was
validated for integration in the brachytherapy workflow.

}, author = {Eric Poulin and Karim Boudam and Csaba Pinter and Samuel Kadoury and Andras Lasso and Gabor Fichtinger and Cynthia Menard} } @article {Suriyakumar2017b, title = {Collision detection for external beam radiation therapy applications in SlicerRT}, year = {2017}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, author = {Vinith M. Suriyakumar and Renee Xu and Csaba Pinter and Gabor Fichtinger} } @article {Herz2017, title = {dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM}, journal = {Cancer Research}, volume = {77}, number = {21}, year = {2017}, month = {11/2017}, pages = {e87{\textendash}e90}, abstract = {

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi. Cancer Res; 77(21); e87{\textendash}90. {\textcopyright}2017 AACR.

}, issn = {0008-5472}, doi = {10.1158/0008-5472.CAN-17-0336}, url = {http://cancerres.aacrjournals.org/content/77/21/e87}, author = {Christian Herz and Jean-ChristopheC. Fillion-Robin and Michael Onken and J{\"o}rg Riesmeier and Andras Lasso and Csaba Pinter and Gabor Fichtinger and Steve Pieper and Clunie, David and Ron Kikinis and Andriy Fedorov} } @conference {sunderland2017a, title = {Fractional labelmaps for computing accurate dose volume histograms}, booktitle = {SPIE Medical Imaging}, year = {2017}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, abstract = {

PURPOSE: In radiation therapy treatment planning systems, structures are represented as parallel 2D contours. For treatment planning algorithms, structures must be converted into labelmap (i.e. 3D image denoting structure inside/outside) representations. This is often done by triangulated a surface from contours, which is converted into a binary labelmap. This surface to binary labelmap conversion can cause large errors in small structures. Binary labelmaps are often represented using one byte per voxel, meaning a large amount of memory is unused. Our goal is to develop a fractional labelmap representation containing non-binary values, allowing more information to be stored in the same amount of memory.

METHODS: We implemented an algorithm in 3D Slicer, which converts surfaces to fractional labelmaps by creating 216 binary labelmaps, changing the labelmap origin on each iteration. The binary labelmap values are summed to create the fractional labelmap. In addition, an algorithm is implemented in the SlicerRT toolkit that calculates dose volume histograms (DVH) using fractional labelmaps.

RESULTS: We found that with manually segmented RANDO{\textregistered} head and neck structures, fractional labelmaps represented structure volume up to 19.07\% (average 6.81\%) more accurately than binary labelmaps, while occupying the same amount of memory. When compared to baseline DVH from treatment planning software, DVH from fractional labelmaps had agreement acceptance percent (1\% ΔD, 1\% ΔV) up to 57.46\% higher (average 4.33\%) than DVH from binary labelmaps.

CONCLUSION: Fractional labelmaps promise to be an effective method for structure representation, allowing considerably more information to be stored in the same amount of memory

}, author = {Kyle R. Sunderland and Csaba Pinter and Andras Lasso and Gabor Fichtinger} } @article {Moult2017, title = {Improved temporal calibration of tracked ultrasound: an open-source solution}, journal = {Journal of Medical Robotics Research}, volume = {2}, number = {04}, year = {2017}, month = {07/2017}, pages = {1750008}, abstract = {

In tracked ultrasound systems, temporal misalignment between image and tracker data results in incorrect image pose. We present a fully automatic temporal calibration. We image a flat plate in water with a tracked probe undergoing periodic uniaxial freehand translation. Using robust line detection scheme, we compute temporal misalignment as difference between probe and corresponding image position. From 240 sequences, standard deviation was under 5ms for standard imaging parameters. Source code is available in Public Library for Ultrasound Research, PLUS (www.plustoolkit.org).

}, author = {Eric Moult and Andras Lasso and Tamas Ungi and Csaba Pinter and Mattea Welch and Gabor Fichtinger} } @article {Sharp2017, title = {Open Source Proton Treatment Planning in 3D Slicer: Status Update}, volume = {4}, number = {1}, year = {2017}, month = {05/2017}, pages = {14-83}, publisher = {International Journal of Particle Therapy: Summer 2017}, author = {Gregory C Sharp and Csaba Pinter and Jan Unkelbach and Gabor Fichtinger} } @conference {Suriyakumar2017a, title = {Open-source software for collision detection in external beam radiation therapy}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, author = {Vinith M. Suriyakumar and Renee Xu and Csaba Pinter and Gabor Fichtinger} } @conference {Poulin2017, title = {Validation Of MRI To US Registration For Focal Hdr Prostate Brachytherapy Treatment}, booktitle = {Annual Scientific Meeting of the American Brachytherapy Society}, volume = {16}, number = {3}, year = {2017}, pages = {S56-S57}, publisher = {Elsevier}, organization = {Elsevier}, address = {Boston, MA, USA}, keywords = {Brachytherapy, contour propagation, MRI, Ultrasound}, issn = {1538-4721}, doi = {http://dx.doi.org/10.1016/j.brachy.2017.04.093}, url = {http://www.brachyjournal.com/article/S1538-4721(17)30154-X/fulltext}, author = {Eric Poulin}, editor = {Karim Boudam and Csaba Pinter and Samuel Kadoury and Andras Lasso and Gabor Fichtinger and Cynthia Menard} } @article {Sunderland2016b, title = {Analysis of dose volume histogram deviations using different voxelization parameters}, year = {2016}, address = {Toronto, Canada}, author = {Kyle R. Sunderland and Csaba Pinter and Andras Lasso and Gabor Fichtinger} } @conference {Alexander2016, title = {Development of 3D Slicer Based Film Dosimetry Analysis}, booktitle = {IC3DDose (9th International Conference on 3D Radiation Dosimetry)}, volume = {847}, year = {2016}, month = {10/2016}, publisher = {IOP Science}, organization = {IOP Science}, address = {Houston, TX, USA}, abstract = {

Radiochromic film dosimetry has been widely adopted in the clinic as it is a convenient option for dose measurement and verification. Film dosimetry analysis is typically performed using expensive commercial software, or custom made scripts in Matlab. However, common clinical film analysis software is not transparent regarding what corrections/optimizations are running behind the scenes. An extension to the open-source medical imaging platform 3D Slicer has been designed and implemented in our centre for film dosimetry analysis. This extension enables importing treatment planning system dose and film imaging data, film calibration, registration, and comparison of 2D dose distributions, enabling greater accessibility to film analysis and higher reliability.

}, author = {Kevin Alexander and Alec Robinson and Csaba Pinter and Gabor Fichtinger and John Schreiner} } @article {Pinter2016, title = {Dynamic management of segmented structures in 3D Slicer}, year = {2016}, month = {03/2016}, author = {Csaba Pinter and Andras Lasso and Gabor Fichtinger} } @conference {sunderland2016a, title = {Effects of voxelization on dose volume histogram accuracy}, booktitle = {SPIE Medical Imaging}, year = {2016}, pages = {97862O{\textendash}97862O}, publisher = {International Society for Optics and Photonics}, organization = {International Society for Optics and Photonics}, abstract = {

PURPOSE: In radiotherapy treatment planning systems, structures of interest such as targets and organs at risk are stored as 2D contours on evenly spaced planes. In order to be used in various algorithms, contours must be converted into binary labelmap volumes using voxelization. The voxelization process results in lost information, which has little effect on the volume of large structures, but has significant impact on small structures, which contain few voxels. Volume differences for segmented structures affects metrics such as dose volume histograms (DVH), which are used for treatment planning. Our goal is to evaluate the impact of voxelization on segmented structures, as well as how factors like voxel size affects metrics, such as DVH.

METHODS: We create a series of implicit functions, which represent simulated structures. These structures are sampled at varying resolutions, and compared to labelmaps with high sub-millimeter resolutions. We generate DVH and evaluate voxelization error for the same structures at different resolutions by calculating the agreement acceptance percentage between the DVH.

RESULTS: We implemented tools for analysis as modules in the SlicerRT toolkit based on the 3D Slicer platform. We found that there were large DVH variation from the baseline for small structures or for structures located in regions with a high dose gradient, potentially leading to the creation of suboptimal treatment plans.

CONCLUSION: This work demonstrates that labelmap and dose volume voxel size is an important factor in DVH accuracy, which must be accounted for in order to ensure the development of accurate treatment plans.\ 

}, author = {Kyle R. Sunderland and 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} } @conference {Alexander2015, title = {3D Slicer Gel Dosimetry Analysis: Validation of the Calibration Process}, booktitle = {World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada}, volume = {51}, year = {2015}, month = {06/2015}, pages = {521-524}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

An extension tailored to dose data processing and analysis has been developed in the open source imaging application 3D Slicer to aid in routine clinical use of gel dosim-etry. This extension allows for registration, calibration, and comparison of 3D gel dosimeter data (imaged using an optical CT scanner) to treatment planning data. In this work, we present the accuracy and reproducibility of the gel dosimeter calibration component of the 3D Slicer extension. We examine the consistency of the calibration curves for a range of electron beam irradiations, and the inter-user variability of the gel dosimeter calibration process.

}, doi = {10.1007/978-3-319-19387-8_128}, url = {http://link.springer.com/chapter/10.1007\%2F978-3-319-19387-8_128}, author = {Kevin Alexander and Csaba Pinter and Jennifer Andrea and Gabor Fichtinger and John Schreiner} } @article {Andrea2015a, title = {Cloud computing of anatomical similarity}, year = {2015}, month = {03/2015}, pages = {35}, address = {London, Canada}, url = {http://www.imno.ca/sites/default/files/IMNO-Conference-Program-Final.pdf}, author = {Jennifer Andrea and Csaba Pinter and Gabor Fichtinger} } @article {Alexander2015a, title = {Cross-Validation of 3D Gamma Comparison Tools}, volume = {42}, number = {15}, year = {2015}, month = {06/2015}, pages = {3385-3385}, publisher = {Medical Physics}, doi = {10.1118/1.4924592}, author = {Kevin Alexander and Christopher Jechel and Csaba Pinter and Greg Salomons and Andras Lasso and Gabor Fichtinger and L. John Schreiner} } @conference {Andrea2015b, title = {Measuring radiation treatment plan similarity in the cloud}, booktitle = {World Congress on Medical Physics and Biomedical Engineering}, volume = {51}, year = {2015}, month = {07/2015}, pages = {432-435}, publisher = {Springer}, organization = {Springer}, address = {Toronto, Canada}, isbn = { 978-3-319-19387-8}, issn = {1680-0737}, doi = {10.1007/978-3-319-19387-8_105}, url = {http://link.springer.com/book/10.1007/978-3-319-19387-8}, author = {Jennifer Andrea and Csaba Pinter and Gabor Fichtinger} } @conference {Pinter2015, title = {Performing radiation therapy research using the open-source SlicerRT toolkit}, booktitle = {World Congress on Medical Physics and Biomedical Engineering}, year = {2015}, month = {07/2015}, publisher = {Springer}, organization = {Springer}, address = {Toronto}, abstract = {

Radiation therapy (RT) is a common treatment option for a wide variety of cancer types. Despite significant improvements in this technique over the past years, software tools for research in RT are limited to either expensive, closed, proprietary applications or heterogeneous sets of open-source software packages with limited scope, reliability, and user support. Our SlicerRT toolkit aspires to overcome these limitations by providing an extensive set of RT research tools leveraging the advanced visualization and image analysis features of its base platform 3D Slicer.

The SlicerRT toolkit comprises of a set of 3D Slicer extensions: SlicerRT core, Matlab Bridge, Multi-dimensional Data, and Gel Dosimetry. The SlicerRT core extension contains 26 modules, many of which provide common RT tools used in most RT research scenarios. Matlab Bridge provides a convenient way for connecting the researchers{\textquoteright} existing MATLAB algorithms to the SlicerRT ecosystem. Multi-dimensional Data offers a feature set for handling multi-dimensional datasets, such as longitudinal studies or 4D data. Finally, Gel Dosimetry facilitates gel dosimetry analysis workflows through a streamlined, workflow-based end-user application. It serves as an example and proof of concept for such applications implementing advanced clinical or research workflows.

Using these open-source software tools makes it possible to conduct cutting edge RT research without parallel development efforts. It acts as a medium into which researchers can integrate their methods into, and which they can use to perform comparative validation, develop novel RT techniques, or transition advanced methods into routine clinical practice.

}, doi = {10.1007/978-3-319-19387-8_152}, author = {Csaba Pinter and Andras Lasso and An Wang and Gregory C Sharp and Kevin Alexander and David Jaffray and Gabor Fichtinger} } @conference {Sunderland2015, title = {Reconstruction of surfaces from planar contours through contour interpolation}, booktitle = {SPIE Medical Imaging 2015}, volume = {9415}, year = {2015}, pages = {94151R-94151R-8}, abstract = {

Segmented structures such as targets or organs at risk are typically stored as 2D contours contained on evenly spaced cross sectional images (slices). Contour interpolation algorithms are implemented in radiation oncology treatment planning software to turn 2D contours into a 3D surface, however the results differ between algorithms, causing discrepancies in analysis. Our goal was to create an accurate and consistent contour interpolation algorithm that can handle issues such as keyhole contours, rapid changes, and branching. This was primarily motivated by radiation therapy research using the open source SlicerRT extension for the 3D Slicer platform. The implemented algorithm triangulates the mesh by minimizing the length of edges spanning the contours with dynamic programming. The first step in the algorithm is removing keyholes from contours. Correspondence is then found between contour layers and branching patterns are determined. The final step is triangulating the contours and sealing the external contours. The algorithm was tested on contours segmented on computed tomography (CT) images. Some cases such as inner contours, rapid changes in contour size, and branching were handled well by the algorithm when encountered individually. There were some special cases in which the simultaneous occurrence of several of these problems in the same location could cause the algorithm to produce suboptimal mesh. An open source contour interpolation algorithm was implemented in SlicerRT for reconstructing surfaces from planar contours. The implemented algorithm was able to generate qualitatively good 3D mesh from the set of 2D contours for most tested structures.

}, doi = {10.1117/12.2081436}, url = {http://dx.doi.org/10.1117/12.2081436}, author = {Kyle R. Sunderland and Woo, Boyeong and Csaba Pinter and Gabor Fichtinger} } @conference {Cifuentes2014a, title = {Customized deformable image registration using open-source software SlicerRT}, booktitle = {American Association Physicists in Medicine (AAPM)}, volume = {41 (abstract in Medical Physics)}, year = {2014}, month = {06/2014}, address = {Austin, TX}, abstract = {
Purpose:

SlicerRT is a flexible platform that allows the user to incorporate the necessaryimages registration\ and processing tools to improve clinical workflow. This work validates the accuracy and the versatility of the deformable\ image registrationalgorithm of the free open-source\ software\ SlicerRT using a deformable physical pelvic phantom versus available commercial\ image\ fusion algorithms.

Methods:

Optical\ camera\ images\ of nonradiopaque markers implanted in an anatomical pelvic phantom were used to measure the ground-truth deformation and evaluate the theoretical deformations for several DIR algorithms. To perform the registration, full and empty bladder\ computed tomography\ (CT)\ images\ of the phantom were obtained and used as fixed and moving\ images,\ respectively. The DIR module, found in SlicerRT, used a B-spline deformable\ image registration\ with multiple optimization parameters that allowed customization of the registration including a regularization term that controlled the amount of local voxel displacement. The virtual deformation field at the center of the phantom was obtained and compared to the experimental ground-truth values. The parameters of SlicerRT were then varied to improve spatial accuracy. To quantify\ imagesimilarity, the mean absolute difference (MAD) parameter using Hounsfield units was calculated. In addition, the Dice coefficient of the contoured rectum was evaluated to validate the strength of the algorithm to transfer anatomical contours.

Results:

Overall, SlicerRT achieved one of the lowest MAD values across the algorithm spectrum, but slightly smaller mean spatial errors in comparison to MIM\ software(MIM). On the other hand, SlicerRT created higher mean spatial errors than Velocity Medical Solutions (VEL), although obtaining an improvement on the DICE to 0.91. The large spatial errors were attributed to the poor contrast in the prostate bladder interface of the phantom.

Conclusion:

Based phantom validation, SlicerRT is capable of achieving comparable DIR accuracy to commercial programs such as MIM and VEL.

}, doi = {http://dx.doi.org/10.1118/1.4888094}, author = {J Cifuentes and N Kirby and Andras Lasso and L Chin and Csaba Pinter and J Pouliot and Gabor Fichtinger and J Pignol} } @conference {Alexander2014, title = {Implementation of an efficient workflow process for gel dosimetry using 3D Slicer}, booktitle = {IC3DDose}, year = {2014}, abstract = {

One challenge in gel dosimetry is the manipulation and analysis of complex data sets from different systems. In this paper, we describe a simple and fast gel dosimetry analysis tool for radiation therapy dose deliveries. Using the open source medical imaging software 3D Slicer, an extension was designed and implemented for the purpose of importing treatment planning system dose, CT imaging from simulation and at treatment, and optical CT gel dosimeter data. The extension also allows for calibration of gel dosimeter data, registration, and comparison of 3D dose distributions. The development of an open source gel dosimetry processing environment may help adoption of gels in the clinic.

}, author = {Kevin Alexander and Csaba Pinter and Jennifer Andrea and Gabor Fichtinger and John Schreiner} } @article {Pinter2014, title = {Improvements in SlicerRT, the radiation therapy research toolkit for 3D Slicer}, year = {2014}, month = {03/2014}, pages = {79}, address = {Toronto, ON, Canada, March 24-25, 2014}, url = {http://www.imno.ca/sites/default/files/2014Proceedings.pdf}, author = {Csaba Pinter and Andras Lasso and An Wang and Gregory C Sharp and David Jaffray and Gabor Fichtinger} } @article {Cifuentes2014, title = {Integration of SlicerRT into the clinical adaptive radiation therapy workflow: deformable image registration algorithm optimization and validation}, year = {2014}, month = {03/2014}, pages = {66}, url = {http://www.imno.ca/sites/default/files/2014Proceedings.pdf}, author = {J Cifuentes and Eric Vorauer and Peggy Le and Daniel Glick and Jonathan Livergant and Csaba Pinter and Andras Lasso and Gabor Fichtinger and Lee Chin} } @article {Glick2014, title = {Interfraction Variation in Dose and Position During the Treatment of Pancreatic Cancer Using Tomotherapy}, year = {2014}, month = {03/2014}, pages = {70}, url = {http://www.imno.ca/sites/default/files/2014Proceedings.pdf}, author = {Daniel Glick and Eric Vorauer and J Cifuentes and Frances Tonolete and Csaba Pinter and Gabor Fichtinger and Andras Lasso and Lee Chin and Hans Chung} } @article {Lasso2014a, title = {PLUS: Open-source toolkit for ultrasound-guided intervention systems}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {61}, number = {10}, year = {2014}, month = {10/2014}, pages = {2527-2537}, issn = {0018-9294}, doi = {10.1109/TBME.2014.2322864}, author = {Andras Lasso and Tamas Heffter and Adam Rankin and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Andrea2014a, title = {Slicelet implementation for gel dosimetry analysis}, year = {2014}, month = {03/2014}, pages = {63}, address = {Toronto, Canada}, url = {http://www.imno.ca/sites/default/files/2014Proceedings.pdf}, author = {Jennifer Andrea and Mattea Welch and Csaba Pinter and Kevin Alexander and John Schreiner and Gabor Fichtinger} } @article {King2014b, title = {A tool for intraoperative visualization of image registration results}, year = {2014}, month = {03/2014}, publisher = {ImNO}, address = {Toronto, Canada, March 24-25}, author = {Franklin King and Andras Lasso and Csaba Pinter and Gabor Fichtinger} } @conference {King2014a, title = {A tool for intraoperative visualization of registration results}, booktitle = {SPIE Medical Imaging 2014}, volume = {9036}, year = {2014}, month = {03/2014}, pages = {90362A}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, California, USA}, doi = {10.1117/12.2043496}, author = {Franklin King and Andras Lasso and Csaba Pinter and Gabor Fichtinger} } @article {Ogilvie2013, title = {Automated Brachytherapy Calibration: System and Phantom Design}, year = {2013}, month = {02/2013}, address = {Toronto, Canada, February 4th-5th 2013}, author = {Ogilvie, Colleen and Martin, Cara and Law, Trevor and Vandersleen, Philip and Csaba Pinter and Adam Rankin and Gabor Fichtinger} } @article {Livergant2013, title = {Investigation of daily deformable image registration for adaptive radiation therapy in head and neck cancer}, year = {2013}, address = {Montreal, QC}, author = {Jonathan Livergant and Eric Vorauer and Andrew Agbanti and Andras Lasso and Csaba Pinter and Gabor Fichtinger and Lee Chin} } @article {Bartha2013a, title = {Open Source Ultrasound Simulation Using Surface Meshes}, year = {2013}, month = {02/2013}, address = {Toronto, Canada, February 4th-5th}, keywords = {PLUS toolkit, surface mesh, training, ultrasound simulation}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @conference {Bartha2013c, title = {Open-Source Surface Mesh-Based Ultrasound Simulator}, booktitle = {CARS 2013}, volume = {8}, year = {2013}, month = {06/2013}, pages = {156}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Bartha2013b, title = {Open-source surface mesh-based ultrasound-guided spinal intervention simulator}, journal = {International Journal of Computer Assisted Radiology and Surgery}, volume = {8}, year = {2013}, month = {06/2013}, pages = {1043-51}, doi = {10.1007/s11548-013-0901-z}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Keri, Zsuzsanna and Gabor Fichtinger} } @article {Bartha2013, title = {The Perk Tutor Training Platform Integrated with Simulated Ultrasound}, year = {2013}, month = {03/2013}, author = {Laura Bartha and Andras Lasso and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Lasso2013a, title = {Prototyping Clinical Applications with the Public Library for Ultrasound (PLUS) Toolkit and 3D Slicer}, volume = {6}, year = {2013}, month = {03/2013}, pages = {34}, address = {Crystal City, VA, USA}, url = {http://www.ncigt.org/publications/item/view/2334}, author = {Andras Lasso and Tamas Ungi and Csaba Pinter and Tamas Heffter and Adam Rankin and Gabor Fichtinger} } @article {Ungi2013a, title = {SlicerIGT: Open-source platform for image-guided needle interventions}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {Tamas Ungi and Andras Lasso and Csaba Pinter and Adam Rankin and Gabor Fichtinger} } @article {Pinter2013a, title = {SlicerRT {\textendash} Radiation therapy research toolkit for 3D Slicer}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {Csaba Pinter and Andras Lasso and An Wang and David Jaffray and Gabor Fichtinger} } @article {Pinter2013b, title = {SlicerRT sets new standard for radiation therapy research tools}, journal = {ECR Today 2013}, year = {2013}, month = {03/2013}, pages = {19}, edition = {Technology Focus}, author = {Csaba Pinter and Andras Lasso and An Wang and David Jaffray and Gabor Fichtinger} } @article {Moult2013b, title = {Temporal calibration of tracked ultrasound}, year = {2013}, month = {02/2013}, address = {Toronto, ON, Canada}, author = {Eric Moult and Tamas Ungi and Csaba Pinter and Mattea Welch and Andras Lasso and Gabor Fichtinger} } @conference {Pinter2012c, title = {Application of the SlicerRT toolkit for image-guided radiation therapy research}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012) - Image-Guidance and Multimodal Dose Planning in Radiation Therapy}, year = {2012}, month = {10/2012}, address = {Nice, France}, author = {Csaba Pinter and Andras Lasso and An Wang and David Jaffray and Gabor Fichtinger} } @conference {Lasso2012d, title = {Implementation of the PLUS open-source toolkit for translational research of ultrasound-guided intervention systems}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI 2012) - Systems and Architectures for Computer Assisted Interventions}, year = {2012}, month = {10/2012}, pages = {1-12}, publisher = {The MIDAS Journal}, organization = {The MIDAS Journal}, address = {Nice, France}, url = {http://hdl.handle.net/10380/3367}, author = {Andras Lasso and Tamas Heffter and Csaba Pinter and Tamas Ungi and Gabor Fichtinger} } @article {Ungi2012c, title = {Perk Tutor: An open-source training platform for ultrasound-guided needle insertions}, journal = {IEEE Trans Biomed Eng}, volume = {59}, year = {2012}, month = {12/2012}, pages = {3475-3481}, issn = {0018-9294}, doi = {10.1109/TBME.2012.2219307}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6304910}, author = {Tamas Ungi and Sargent, D. and Eric Moult and Andras Lasso and Csaba Pinter and McGraw, Robert C and Gabor Fichtinger} } @article {Lasso2012a, title = {PLUS: An open-source toolkit for ultrasound-guided intervention systems development}, year = {2012}, month = {02/2012}, address = {Toronto, ON, Canada}, author = {Andras Lasso and Tamas Heffter and Csaba Pinter and Tamas Ungi and Thomas K. Chen and Alexis Boucharin and Gabor Fichtinger} } @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} } @article {Pinter2012a, title = {SlicerRT {\textendash} Radiotherapy toolkit for 3D Slicer}, year = {2012}, month = {02/2012}, address = {Toronto, ON, Canada}, keywords = {Radiotherapy}, author = {Csaba Pinter and Andras Lasso and An Wang and Gabor Fichtinger} } @article {Chen2011, title = {Automated Intraoperative Calibration for Prostate Cancer Brachytherapy}, journal = {Medical Physics}, volume = {38}, year = {2011}, month = {11/2011}, pages = {6285-6299}, doi = {10.1118/1.3651690}, author = {Thomas K. Chen and Tamas Heffter and Andras Lasso and Csaba Pinter and Purang Abolmaesumi and E. Clif Burdette and Gabor Fichtinger} } @article {Lasso2011c, title = {PLUS: An open-source toolkit for developing ultrasound-guided intervention systems}, volume = {4}, year = {2011}, month = {10/2011}, pages = {103}, address = {Arlington VA, October 12-13, 2011}, author = {Andras Lasso and Tamas Heffter and Csaba Pinter and Tamas Ungi and Thomas K. Chen and Alexis Boucharin and Gabor Fichtinger} }