Csaba Pinter

@article{fichtinger2022c,

title = {SlicerHeart: An open-source computing platform for cardiac image analysis and modeling},

author = {Andras Lasso and Christian Herz and Hannah Nam and Alana Cianciulli and Steve Pieper and Simon Drouin and Csaba Pinter and Samuelle St-Onge and Chad Vigil and Stephen Ching and Kyle Sunderland and Gabor Fichtinger and Ron Kikinis and Matthew A Jolley},

url = {https://www.frontiersin.org/articles/10.3389/fcvm.2022.886549/full},

year  = {2022},

date = {2022-01-01},

volume = {9},

pages = {886549},

publisher = {Frontiers},

abstract = {Cardiovascular disease is a significant cause of morbidity and mortality in the developed world. 3D imaging of the heart’s structure is critical to the understanding and treatment of cardiovascular disease. However, open-source tools for image analysis of cardiac images, particularly 3D echocardiographic (3DE) data, are limited. We describe the rationale, development, implementation, and application of SlicerHeart, a cardiac-focused toolkit for image analysis built upon 3D Slicer, an open-source image computing platform. We designed and implemented multiple Python scripted modules within 3D Slicer to import, register, and view 3DE data, including new code to volume render and crop 3DE. In addition, we developed dedicated workflows for the modeling and quantitative analysis of multi-modality image-derived heart models, including heart valves. Finally, we created and integrated new functionality to facilitate the planning of cardiac interventions and surgery. We demonstrate application of SlicerHeart to a diverse range of cardiovascular modeling and simulation including volume rendering of 3DE images, mitral valve modeling, transcatheter device modeling, and planning of complex surgical intervention such as cardiac baffle creation. SlicerHeart is an evolving open-source image processing platform based on 3D Slicer initiated to support the investigation and treatment of congenital heart disease. The technology in SlicerHeart provides a robust foundation for 3D image-based investigation in cardiovascular medicine.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2021g,

title = {Simulation of delivery of clip-based therapies within multimodality images to facilitate preprocedural planning},

author = {Alana Cianciulli and Andras Lasso and Csaba Pinter and Stephen Ching and Reena M Ghosh and Tiffany Chen and Christian Herz and Chad Vigil and Simon Drouin and Lindsay S Rogers and Michael D Quartermain and David M Biko and Kevin K Whitehead and Gabor Fichtinger and Matthew J Gillespie and Matthew A Jolley},

url = {https://www.onlinejase.com/article/S0894-7317(21)00588-5/abstract},

year  = {2021},

date = {2021-01-01},

journal = {Journal of the American Society of Echocardiography},

volume = {34},

issue = {10},

pages = {1111-1114},

publisher = {Elsevier},

abstract = {Brief Research Communications 1111 repair (TEER) has emerged as a therapeutic option for the treatment of severe mitral regurgitation and tricuspid regurgitation and avoids the morbidity and mortality associated with open heart surgery. 1, 2 One challenge to the successful application of this therapy is delivering the clip to a precise location within the constraints of the unique anatomy and resulting geometry of an individual patient. Threedimensional echocardiography (3DE) is typically used to plan and execute TEER using catheter delivery systems designed to access a specific anatomic location (mitral valve, tricuspid valve) via a known path. These systems are then empirically validated and iteratively improved via large clinical trials and clinical practice. However, there has been little work on modeling the catheter path and approach to deliver the clip to the desired location in atypical or surgically altered …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2021o,

title = {Automated catheter segmentation using 3D ultrasound images in high-dose-rate prostate brachytherapy},

author = {Zoe Hu and Harry Brastianos and Tamas Ungi and Csaba Pinter and Tim Olding and Martin Korzeniowski and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11598/115980Z/Automated-catheter-segmentation-using-3D-ultrasound-images-in-high-dose/10.1117/12.2581966.short},

year  = {2021},

date = {2021-01-01},

volume = {11598},

pages = {252-259},

publisher = {SPIE},

abstract = {PURPOSE 

High-dose-rate brachytherapy (HDR-BT) is an important treatment modality for prostate cancer that maximizes radiation dose to cancerous tissue while sparing surrounding organs. Currently, treatment planning during HDR-BT is manually completed by medical physicists, a time-consuming and observer dependent process. We propose using deep learning through a U-Net architecture to automatically segment catheters in HDR prostate brachytherapy treatment planning. 

METHODS 

3D Ultrasound data along with the corresponding manual contours were obtained from 49 patients undergoing HDR prostate brachytherapy. The dataset was preprocessed and then exported for training and evaluation. The resulting model was assessed both quantitatively with binary segmentation metrics and qualitatively through 3D reconstructions. 

RESULTS 

The output segmentations demonstrated consistency on …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinter2020a,

title = {Using Fuzzy Logics to Determine Optimal Oversampling Factor for Voxelizing 3D Surfaces in Radiation Therapy},

author = {Csaba Pinter and Tim Olding and L. John Schreiner and Gabor Fichtinger},

url = {https://link.springer.com/article/10.1007/s00500-020-05126-w

https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/03/Pinter2020a_0.pdf},

doi = {10.1007/s00500-020-05126-w},

year  = {2020},

date = {2020-06-01},

urldate = {2020-06-01},

journal = {Soft Computing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinter2020,

title = {SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality},

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},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/03/Pinter2020a_0.pdf},

doi = {10.1109/TMRB.2020.2983199},

year  = {2020},

date = {2020-03-01},

urldate = {2020-03-01},

journal = {IEEE Transactions on Medical Robotics and Bionics},

volume = {2},

number = {2},

pages = {108-117},

abstract = {<p>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.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020n,

title = {Open source software platform for interstitial ablation treatment planning},

author = {Alice Santilli and Csaba Pinter and Bote Jiang and Gernot Kronreif and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11315/1131526/Open-source-software-platform-for-interstitial-ablation-treatment-planning/10.1117/12.2549577.short},

year  = {2020},

date = {2020-01-01},

volume = {11315},

pages = {566-571},

publisher = {SPIE},

abstract = {PURPOSE 

There are several interstitial (needle based) image-guided ablation planning systems available, but most of them are closed or unsupported. We propose an open source software platform for the planning of image-guided interstitial ablation procedures, providing generic functionality and support for specialized plug-ins. 

METHODS 

The patient’s image data is loaded or streamed into the system and the relevant structures are segmented. The user places fiducial points as ablation needle entries and tips, sets the ablation times, and the thermal dose is calculated by a dose engine. The thermal dose is then visualized on the 2D image slices and 3D rendering using a combination of isodose lines and surfaces. Quantitative feedback is provided by dose volume histograms. The treatment plan can be iteratively edited until satisfactory dose distribution is achieved. We performed a usability study with eight …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020q,

title = {Using fuzzy logics to determine optimal oversampling factor for voxelizing 3D surfaces in radiation therapy},

author = {Csaba Pinter and Tim Olding and L John Schreiner and Gabor Fichtinger},

url = {https://link.springer.com/article/10.1007/s00500-020-05126-w},

year  = {2020},

date = {2020-01-01},

journal = {Soft Computing},

volume = {24},

pages = {18959-18970},

publisher = {Springer Berlin Heidelberg},

abstract = {Voxelizing three-dimensional surfaces into binary image volumes is a frequently performed operation in medical applications. In radiation therapy (RT), dose-volume histograms (DVHs) calculated within such surfaces are used to assess the quality of an RT treatment plan in both clinical and research settings. To calculate a DVH, the 3D surfaces need to be voxelized into binary volumes. The voxelization parameters may considerably influence the output DVH. An effective way to improve the quality of the voxelized volume (i.e., increasing similarity between that and the original structure) is to apply oversampling to increase the resolution of the output binary volume. However, increasing the oversampling factor raises computational and storage demand. This paper introduces a fuzzy inference system that determines an optimal oversampling factor based on relative structure size and complexity, finding the balance …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020,

title = {SlicerVR for medical intervention training and planning in immersive virtual reality},

author = {Csaba Pinter and Andras Lasso and Saleh Choueib and Mark Asselin and Jean-Christophe Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and Matthew A Jolley and Gabor Fichtinger},

url = {https://ieeexplore.ieee.org/abstract/document/9047949/},

year  = {2020},

date = {2020-01-01},

journal = {IEEE transactions on medical robotics and bionics},

volume = {2},

issue = {2},

pages = {108-117},

publisher = {IEEE},

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 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fedorov2020,

title = {Quantitative Imaging Informatics for Cancer Research},

author = {Andriy Fedorov and Reinhard Beichel and Jayashree Kalpathy-Cramer and David Clunie and Michael Onken and Jörg 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},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Fedorov2020.pdf},

doi = {https://doi.org/10. 1200/CCI.19.00165},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {JCO Clinical Cancer Informatics},

volume = {4},

pages = {444-453.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinter2019,

title = {Polymorph Segmentation Representation for Medical Image Computing},

author = {Csaba Pinter and Andras Lasso and Gabor Fichtinger},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Pinter2019_Manuscript.pdf},

doi = {https://doi.org/10.1016/j.cmpb.2019.02.011},

year  = {2019},

date = {2019-02-01},

urldate = {2019-02-01},

journal = {Computer Methods and Programs in Biomedicine},

volume = {171},

pages = {19-26},

abstract = {<p><strong>Background and Objective: </strong>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: <a name="OLE_LINK3"></a><a name="OLE_LINK2">conversion </a>method selection, data provenance, data consistency, and coherence of in-memory objects. <strong>Methods:</strong> 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. <strong>Results: </strong>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. <strong>Conclusions:</strong>  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’s BSD-style open-source license allows complete freedom of use of the library.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2019b,

title = {Polymorph segmentation representation for medical image computing},

author = {Csaba Pinter and Andras Lasso and Gabor Fichtinger},

url = {https://www.sciencedirect.com/science/article/pii/S0169260718313038},

year  = {2019},

date = {2019-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {171},

pages = {19-26},

publisher = {Elsevier},

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 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2019j,

title = {Affordable medical ultrasound navigation training},

author = {Guillermo V Socorro-Marrero and Carlos Luque and Csaba Pinter and Babacar Diao and Tamas Ungi and Andras Lasso and Gabor Fichtinger and Juan Ruiz-Alzola},

url = {https://ieeexplore.ieee.org/abstract/document/9033036/},

year  = {2019},

date = {2019-01-01},

pages = {1-4},

publisher = {IEEE},

abstract = {In recent years, image-assisted medical procedures have been widely adopted, since they reduce patient risk and medical cost. The use of ultrasound imaging is noteworthy, as it is safe and easy to acquire. In medical intervention navigation systems, image guidance is combined with real-time position tracking of interventional tools. These techniques allow to improve the safety and accuracy of the procedures. However, the complexity of the systems and equipment cost hinder their adaptation, especially in regions with limited resources. In this paper we report part of our collaborative work within the framework of the European INTERREG MACbioIDi project, devoted to promoting sustainable development through medical technology in some African countries, with the participations of collaborators from Europe, Africa and the Americas. To this extent a medical technology training and development hub has been set …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2019f,

title = {Evaluation of 3D slicer as a medical virtual reality visualization platform},

author = {Saleh Choueib and Csaba Pinter and Andras Lasso and Jean-Christophe Fillion-Robin and Jean-Batiste Vimort and Ken Martin and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10951/1095113/Evaluation-of-3D-slicer-as-a-medical-virtual-reality-visualization/10.1117/12.2513053.short},

year  = {2019},

date = {2019-01-01},

volume = {10951},

pages = {279-286},

publisher = {SPIE},

abstract = {PURPOSE 

There is a lack of open-source or free virtual reality (VR) software that can be utilized for research by medical professionals and researchers. We propose the design and implementation of such software. We also aim to assess the feasibility of using VR as a modality for navigating 3D visualizations of medical scenes. 

METHODS 

To achieve our goal, we added VR capabilities to the open-source medical image analysis and visualization platform, 3D Slicer. We designed the VR extension by basing the software architecture on VTK’s vtkRenderingOpenVR software module. We extended this module by adding features such as full interactivity between 3D Slicer and the VR extension during VR use, variable volume rendering quality based on user headset motion etc. Furthermore, the VR extension was tested in a feasibility study in which participants were asked to complete specific tasks using bot the …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alexander2018,

title = {Streamlined open-source gel dosimetry analysis in 3D slicer},

author = {Kevin Alexander and Csaba Pinter and Gabor Fichtinger and Tim Olding and L. John Schreiner},

url = {http://stacks.iop.org/2057-1976/4/i=4/a=045041

https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Alexander2018.pdf},

doi = {https://doi.org/10.1088/2057-1976/aad0cf},

year  = {2018},

date = {2018-07-01},

urldate = {2018-07-01},

journal = {Biomedical Physics & Engineering Express},

volume = {4},

number = {4},

pages = {045041},

abstract = {<p>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.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lasso2018,

title = {Interaction with Volume-Rendered Three-Dimensional Echocardiographic Images in Virtual Reality},

author = {Andras Lasso and HannahH Nam and Patrick V. 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},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {J Am Soc Echocardiogr},

volume = {31},

number = {10},

pages = {1158-60–},

abstract = {<p>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).</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2018e,

title = {Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy},

author = {Eric Poulin and Karim Boudam and Csaba Pinter and Samuel Kadoury and Andras Lasso and Gabor Fichtinger and Cynthia Ménard},

url = {https://www.sciencedirect.com/science/article/pii/S1538472117305391},

year  = {2018},

date = {2018-01-01},

journal = {Brachytherapy},

volume = {17},

issue = {2},

pages = {283-290},

publisher = {Elsevier},

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, 15 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 imager. 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, which can compute rigid and deformable transformations. An extension of 3D Slicer, SlicerRT, allows import of 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 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2018f,

title = {Interaction with volume-rendered three-dimensional echocardiographic images in virtual reality},

author = {Andras Lasso and Hannah H Nam and Patrick V Dinh and Csaba Pinter and Jean-Christophe Fillion-Robin and Steve Pieper and Sankhesh Jhaveri and Jean-Baptiste Vimort and Ken Martin and Mark Asselin and Francis X McGowan and Ron Kikinis and Gabor Fichtinger and Matthew A Jolley},

url = {https://www.onlinejase.com/article/S0894-7317(18)30343-2/abstract},

year  = {2018},

date = {2018-01-01},

journal = {Journal of the American Society of Echocardiography},

volume = {31},

issue = {10},

pages = {1158-1160},

publisher = {Elsevier},

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). 

Stereoscopic virtual reality (VR) is developing rapidly but is being driven by the video gaming industry, with features not directly applicable to the visualization of …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Herz2017,

title = {dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM},

author = {Christian Herz and Jean-ChristopheC. Fillion-Robin and Michael Onken and Jörg Riesmeier and Andras Lasso and Csaba Pinter and Gabor Fichtinger and Steve Pieper and David Clunie and Ron Kikinis and Andriy Fedorov},

url = {http://cancerres.aacrjournals.org/content/77/21/e87

https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Herz2017_1.pdf},

doi = {10.1158/0008-5472.CAN-17-0336},

issn = {0008-5472},

year  = {2017},

date = {2017-11-01},

urldate = {2017-11-01},

journal = {Cancer Research},

volume = {77},

number = {21},

pages = {e87–e90},

abstract = {<p>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); e87–90. ©2017 AACR.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Poulin2017b,

title = {Validation of MRI to TRUS registration for HDR prostate brachytherapy},

author = {Eric Poulin and Karim Boudam and Csaba Pinter and Samuel Kadoury and Andras Lasso and Gabor Fichtinger and Cynthia Menard},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Poulin2018.pdf},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Brachytherapy},

volume = {17},

number = {2},

pages = {283-290},

abstract = {<p>Purpose: The objective of this study was to develop and<br /> 

validate an open-source module for MRI to transrectal ultrasound (TRUS)<br /> 

registration to support tumor-targeted prostate brachytherapy.</p> 

<p>Methods and Materials: In this study, fifteen patients with prostate<br /> 

cancer lesions visible on multiparametric MRI were selected for the<br /> 

validation. T2-weighted images with 1 mm isotropic voxel size and<br /> 

diffusion weighted images were acquired on a 1.5T Siemens. Three<br /> 

dimensional (3D) TRUS images with 0.5 mm slice thickness were acquired.<br /> 

The investigated registration module was incorporated in the open-source<br /> 

3D Slicer platform and can compute rigid and deformable transformations.<br /> 

An extension of 3D Slicer, SlicerRT, allows import and export to DICOM-RT<br /> 

formats. For validation, similarity indices, prostate volumes and<br /> 

centroid positions were determined in addition to registration errors for<br /> 

common 3D points identified by an experienced radiation oncologist.</p> 

<p>Results: The average time to compute the registration was 35±3 seconds.<br /> 

For the deformable and rigid registration respectively, Dice similarity<br /> 

coefficients were 0.93±0.01 and 0.87±0.05 while the 95% Hausdorff<br /> 

distance was 2.2±0.3 and 4.2±1.0 mm. MRI volumes obtained after the rigid<br /> 

and deformable registration were not statistically different (p>0.05)<br /> 

from reference TRUS volumes. For the deformable and rigid registration<br /> 

respectively, 3D distance error between centroid positions were 0.4±0.1<br /> 

and 2.1±1.0 mm while registration errors between common points were<br /> 

2.3±1.1 and 3.5±3.2 mm. Deformable registration was found significantly<br /> 

better (p<0.05) than rigid registration in all parameters.</p> 

<p>Conclusions: An open-source MRI to TRUS registration platform was<br /> 

validated for integration in the brachytherapy workflow.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2017,

title = {DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM},

author = {Christian Herz and Jean-Christophe Fillion-Robin and Michael Onken and Jörg Riesmeier and Andras Lasso and Csaba Pinter and Gabor Fichtinger and Steve Pieper and David Clunie and Ron Kikinis and Andriy Fedorov},

url = {https://aacrjournals.org/cancerres/article-abstract/77/21/e87/662591},

year  = {2017},

date = {2017-01-01},

journal = {Cancer research},

volume = {77},

issue = {21},

pages = {e87-e90},

publisher = {American Association for Cancer Research},

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 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2017i,

title = {Validation of MRI to US registration for focal HDR prostate brachytherapy},

author = {Eric Poulin and Karim Boudam and Csaba Pinter and Samuel Kadoury and Andras Lasso and Gabor Fichtinger and Cynthia Ménard},

url = {https://www.brachyjournal.com/article/S1538-4721(17)30154-X/abstract},

year  = {2017},

date = {2017-01-01},

journal = {Brachytherapy},

volume = {16},

issue = {3},

pages = {S56-S57},

publisher = {Elsevier},

abstract = {Purpose 

High dose rate (HDR) prostate brachytherapy can achieve long-term disease control. Multiparametric Magnetic Resonance Imaging (mpMRI) allows identification of gross tumor (GTV) in order to boost or target lesions. However, a significant number of clinics are using ultrasound (US) as their planning imaging modality due to its low cost and real time capability; but can’t identify the position of the GTV. Therefore, a registration is needed between mpMRI and US images to accurately delineate the GTV. The goal of the study was to develop and validate a new 3D Slicer MRI to US registration module for focal HDR prostate brachytherapy treatment. 

Materials and Methods 

In this study, eleven patients with prostate cancer who underwent HDR brachytherapy, with lesions visible on mpMRI, were selected for the validation. T2-weighted 3D variable-flip-angle TSE images with 1mm isotropic voxel and diffusion …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kapur2016,

title = {Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience},

author = {Tina Kapur and Steve Pieper and Andras Lasso and Tamas Ungi and Csaba Pinter and Gabor Fichtinger and Ron Kikinis},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Kapur2016.pdf},

doi = {http://doi.org/10.1016/j.media.2016.06.035},

year  = {2016},

date = {2016-10-01},

urldate = {2016-10-01},

journal = {Medical Image Analysis},

volume = {33},

pages = {176-180},

abstract = {<p><span style="color:rgb(46, 46, 46); font-family:arial,helvetica,lucida sans unicode,microsoft sans serif,segoe ui symbol,stixgeneral,cambria math,arial unicode ms,sans-serif; font-size:16px">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—VTK, ITK, CMake, CDash, DCMTK—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 (“Open Science”); 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.</span></p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2015e,

title = {3D slicer gel dosimetry analysis: validation of the calibration process},

author = {Kevin M Alexander and Csaba Pinter and Jennifer Andrea and Gabor Fichtinger and L John Schreiner},

url = {https://link.springer.com/chapter/10.1007/978-3-319-19387-8_128},

year  = {2015},

date = {2015-01-01},

pages = {521-524},

publisher = {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 dosimetry. 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lasso2014a,

title = {PLUS: Open-source toolkit for ultrasound-guided intervention systems},

author = {Andras Lasso and Tamas Heffter and Adam Rankin and Csaba Pinter and Tamas Ungi and Gabor Fichtinger},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Lasso2014a-manuscript.pdf},

doi = {10.1109/TBME.2014.2322864},

issn = {0018-9294},

year  = {2014},

date = {2014-10-01},

urldate = {2014-10-01},

journal = {IEEE Transactions on Biomedical Engineering},

volume = {61},

number = {10},

pages = {2527-2537},

keywords = {},

pubstate = {published},

tppubtype = {article}

}