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Unified Framework for Development, Deployment and Robust Testing Of
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Boise State University - ScholarWorks Boise State University ScholarWorks Computer Science Faculty Publications and Department of Computer Science Presentations 3-1-2011 Unified rF amework for Development, Deployment and Robust Testing of Neuroimaging Algorithms Alark Joshi Boise State University Dustin Scheinost Yale University Hirohito Okuda GE Healthcare Dominique Belhachemi Yale University Isabella Murphy Yale University See next page for additional authors This is an author-produced, peer-reviewed version of this article. The final publication is available at www.springerlink.com. Copyright restrictions may apply. DOI: 10.1007/s12021-010-9092-8 Authors Alark Joshi, Dustin Scheinost, Hirohito Okuda, Dominique Belhachemi, Isabella Murphy, Lawrence H. Staib, and Xenophon Papademetris This article is available at ScholarWorks: http://scholarworks.boisestate.edu/cs_facpubs/5 Unified framework for development, deployment and robust testing of neuroimaging algorithms Alark Joshi · Dustin Scheinost · Hirohito Okuda · Dominique Belhachemi · Isabella Murphy · Lawrence H. Staib · Xenophon Papademetris Received: date / Accepted: date Abstract Developing both graphical and command- 1 Introduction line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet Image analysis algorithms are typically developed to their potential only if they can be easily and frequently address a particular problem within a specific domain used by their intended users. Deployment of a large (functional MRI, cardiac, image-guided intervention plan- suite of such algorithms on multiple platforms requires ning and monitoring, etc.). Many of these algorithms consistency of user interface controls, consistent results are rapidly prototyped and developed without consid- across various platforms and thorough testing. -
S O F T W a R E D E V E L O P E R ' S Q U a R T E R
SOFTWARE DEVELOPER’S QUARTERLY Issue 12• Jan 2010 MIDAS 2.4 RELEASED AS OPEN SOURCE Editor’s Note ........................................................................... 1 Kitware is proud to announce the release of MIDAS 2.4, a major release implementing more than 20 new features. We Recent Releases ..................................................................... 1 are also happy to announce that the MIDAS source-code is now freely available under an unrestricted (BSD) license. A Synthetic LiDAR Scanner for VTK ..................................... 3 New Variational Level-Set Classes with Region Fitting Energy in ITK ......................................................................... 6 Alternative Memory Models for ITK..................................... 9 N3 Implementation for MRI Bias Field Correction ............ 11 Exporting Contours to DICOM-RTSTRUCT ......................... 13 Kitware News ...................................................................... 15 Kitware is pleased to present a special edition of the Source which features several of the strongest Insight Journal submissions from 2009. The Insight Journal was designed Improved image gallery with color selection to provide a realistic support system for disseminating sci- entific research in the medical image processing domain. For the past year MIDAS, Kitware’s digital archiving and Recognizing the need for a mechanism whereby the medical distributed processing system, has been generating a lot of image analysis community can collectively share their -
Journal of Biomedical Engineering and Medical Imaging, Volume 3, No 6, December(2016), Pp 96-104
` VOLUME 3 ISSUE 6 Advantages and Disadvantages of using Third-party software in the development of the CAS_Annotate and CAS_Navigate Medical Applications 1João Fradinho Oliveira 1C3i/Instituto Politécnico de Portalegre, Portalegre, Portugal; [email protected] ABSTRACT This paper address the main design decision issues taken when using third party libraries in the creation of two medical applications [1] that specifically require editing or creating geometry from CT images (CAS_Annotate) and interactive 3D visualization (CAS_Navigate). Whilst the purpose of the first application was to research different 3D reconstruction algorithms, the second application was created to research different visual metaphors and the reconstructions themselves. This paper weights aspects such as the learning curve time versus coding in-house time, robustness and possible customization. In theory both applications could have been developed within the same IGTSK [2] framework, but the available project time and the development of different phases of the project made that impossible, instead a black box approach of using IGSTK's 3D Msh format was crucial to import algorithm results tested with a simple GLUT application, thus allowing development to be made in parallel. Keywords: Image guided surgery; 3D reconstruction; IGSTK. 9 Introduction Writing applications with third party software has always had many benefits such as access to functonality that would be prohibitive to implement in the time frame of a project, but also the known drawbacks regarding documentation and indeed the learning curve to be able to master and change those solutions at the required level for specific project needs. This paper outlines the requirements of two medical applications [1] CAS_Annotate (a tool that allows one to manually segment/edit contours of objects of interest in CT images and test-bed different 3D reconstruction algorithms) and CAS_Navigate (a tool that provides a road-map of pre-operative geometry of organs and vascular structures intraoperatively). -
Quantifying Anatomical Shape with Slicersalt
SOURCEA PUBLICATION FOR SOFTWARE DEVELOPERS Issue 44 Quantifying Anatomical Shape p.3 with SlicerSALT CONTENTS Kitware Source contains information on open source software. Since 2006, its articles have shared first-hand experiences from Kitware team members and those outside the company’s offices who use and/or develop platforms such as CMake, the Visualization Toolkit, ParaView, the Insight Segmentation and Registration Toolkit, Resonant and the Kitware Image and Video Exploitation and Retrieval toolkit. Readers who wish to share their own experiences or subscribe to the publication can connect with the Kitware Source editor at [email protected]. Kitware Source comes in multiple forms. Kitware mails hard p.3 copies to addresses in North America, and it publishes each issue as a series of posts on https://blog.kitware.com. GRAPHIC DESIGNER QUANTIFYING ANATOMICAL Steve Jordan SHAPE WITH SLICERSALT EDITORS Sandy McKenzie Mary Elise Dedicke GRAND OPENING PHOTOGRAPHER p.5 Elizabeth Fox Photography This work is licensed under an Attribution 4.0 International 3D SLICER AND VIRTUAL (CC BY 4.0) License. INSECT DISSECTION Kitware, ParaView, CMake, KiwiViewer and VolView are registered trademarks of Kitware, Inc. All other trademarks are property of their respective owners. COVER CONTENT Stanford Bunny image generated with SlicerSALT’s Shape Analysis Module. See “Quantifying Anatomical Shape with p.8 SlicerSALT,” which begins on page three, for Stanford bunny meshes. KITWARE NEWS 2 QUANTIFYING ANATOMICAL SHAPE WITH SLICERSALT Beatriz Paniagua Two years ago, the National Institute of Biomedical Imaging and Bioengineering funded an initiative to create open source software to enable biomedical researchers to generate shape analysis measurements from their medical images. -
Open-Source Toolkit for Ultrasound-Guided
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1 PLUS: open-source toolkit for ultrasound-guided intervention systems Andras Lasso, Member, IEEE, Tamas Heffter, Adam Rankin, Csaba Pinter, Tamas Ungi, and Gabor Fichtinger, Senior Member, IEEE Abstract—A variety of advanced image analysis methods have real-time US images with pre-operative computed tomography been under development for ultrasound-guided interventions. (CT) or magnetic resonance (MR) images ([5], [6], [7]). Unfortunately, the transition from an image analysis algorithm Such image fusion has already been developed for some to clinical feasibility trials as part of an intervention system requires integration of many components, such as imaging and clinical applications with the help of US tracking, and it has tracking devices, data processing algorithms, and visualization the potential to transform the way many other radiological software. The objective of our work is to provide a freely available interventions are performed. Also, US imaging has a limited open-source software platform - PLUS: Public software Library field of view. US may fail to show the necessary anatomical for Ultrasound - to facilitate rapid prototyping of ultrasound- context for certain procedures, e.g., identification of a spinal guided intervention systems for translational clinical research. PLUS provides a variety of methods for interventional tool pose segment is difficult from just one US image. Tracked US can and ultrasound image acquisition from a wide range of tracking be extended by stitching together many US image slices and and imaging devices, spatial and temporal calibration, volume reconstructing them in a larger 3D image volume [8]. reconstruction, simulated image generation, and recording and The second major difficulty with US guidance is the co- live streaming of the acquired data. -
Recent Developments in Free Medical Imaging Software
Recent Developments in Free Medical Imaging Software OrthancCon I, 2019 Andrew Crabb The Johns Hopkins University I Do Imaging Why Free Medical Imaging Software? Why Use It? Why Write It? Medical imaging is well-served by free software Recognition and publicity Benefits from collaborative imaging community Free testing by demanding users Source code often available Contributions and improvements Can address specialist/niche/research needs Sometimes required by sponsor Imaging software is competing for the user’s most valuable asset: time Today’s users are accustomed to high-quality free software Many imaging areas are served by multiple free applications Only the best software becomes self-sustaining Distributions Source Virtual Machines GitHub/BitBucket repo Docker/DockerHub • hg clone bitbucket.org/sjodogne/orthanc • docker run jodogne/orthanc Vagrant/VirtualBox • git clone xnat.git; Platform Specific ./run xnat setup HomeBrew (Mac) • brew install dcmtk apt/yum (Linux) Language Specific • apt-get install Pip (Python) python-dicom • pip search nifti # (12 results) zypper (openSUSE) npm/yarn (Node JS) • zypper install • npm search dicom # (24 results) orthanc Chocolatey (Windows) DICOM Libraries DCMTK (OFFIS) • C++ ‘reference’ DICOM library • Steady enhancements since 2003 • Command line utilities dcm4che (dcm4che.org) • Java DICOM toolkit since ca. 2000 • Many command line applications • Adding DICOMWeb capabilities GDCM (Mathieu Malaterre) • Grassroots DICOM • C++, binds to Python, C#, Java, PHP • SCU network operations DICOM Libraries -
Captura, Visualización Y Extracción Aproximada De Contornos De Imágenes 3D De Arterias Simples Miguel Angel Castañeda Zambra
CAPTURA, VISUALIZACIÓN Y EXTRACCIÓN APROXIMADA DE CONTORNOS DE IMÁGENES 3D DE ARTERIAS SIMPLES MIGUEL ANGEL CASTAÑEDA ZAMBRANO UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERIA DEPARTAMENTO DE SISTEMAS Y COMPUTACIÓN Bogotá, 2004 Miguel Angel Castañeda Zambrano CAPTURA, VISUALIZACIÓN Y EXTRACCIÓN APROXIMADA DE CONTORNOS DE IMÁGENES 3D DE ARTERIAS SIMPLES Tesis de Grado Trabajo de grado presentado como requisito parcial para optar al titulo de Ingeniero de Sistemas y Computación Asesor: Tiberio Hernández UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERIA DEPARTAMENTO DE SISTEMAS Y COMPUTACIÓN Bogotá, febrero de 2004 Gracias a mi familia Y a todos los que lo hicieron posible ISC-2003-1-8 CONTENIDO 1. Introducción .......................................................................................... 2 1.1 Descripción del problema .............................................................. 3 1.2 Organización del documento ......................................................... 3 2. Contexto ............................................................................................... 5 2.1 Contexto médico ............................................................................ 5 2.1.1 Arterias principales ............................................................... 5 2.1.2 Enfermedades arteriales: Estenosis .................................... 9 2.2 Contexto mecánico ......................................................................... 9 2.2.1 Modelos computacionales .................................................... 10 2.3 -
3D Slicer Documentation
3D Slicer Documentation Slicer Community Sep 24, 2021 CONTENTS 1 About 3D Slicer 3 1.1 What is 3D Slicer?............................................3 1.2 License..................................................4 1.3 How to cite................................................5 1.4 Acknowledgments............................................7 1.5 Commercial Use.............................................8 1.6 Contact us................................................9 2 Getting Started 11 2.1 System requirements........................................... 11 2.2 Installing 3D Slicer............................................ 12 2.3 Using Slicer............................................... 14 2.4 Glossary................................................. 19 3 Get Help 23 3.1 I need help in using Slicer........................................ 23 3.2 I want to report a problem........................................ 23 3.3 I would like to request enhancement or new feature........................... 24 3.4 I would like to let the Slicer community know, how Slicer helped me in my research......... 24 3.5 Troubleshooting............................................. 24 4 User Interface 27 4.1 Application overview........................................... 27 4.2 Review loaded data............................................ 29 4.3 Interacting with views.......................................... 31 4.4 Mouse & Keyboard Shortcuts...................................... 35 5 Data Loading and Saving 37 5.1 DICOM data.............................................. -
Input Preparation, Data Visualization & Analysis
Input Preparation, Data Visualization & Analysis June 8, 2013 LA-SiGMA Baton Rouge, LA Dr. Marcus D. Hanwell [email protected] http://openchemistry.org/ 1 Outline • Introduction • Kitware • Open Chemistry • Avogadro 2 • MoleQueue • MongoChem • The Future • Summary 2 Introduction • User-friendly desktop integration with – Computational codes – HPC/cloud resources – Database/informatics resources 3 Introduction • Bringing real change to chemistry – Open-source frameworks – Developed openly – Cross-platform compatibility – Tested and verified – Contribution model – Supported by Kitware experts • Liberally-licensed to facilitate research 4 Open Chemistry Development Team • Inter-disciplinary team at Kitware • The first three worked on open-source chemistry in their spare time • The final two are computer scientists with years of open-source experience • Seeking partners in industry & research, labs 5 Outline • Introduction • Kitware • Open Chemistry • Avogadro 2 • MoleQueue • MongoChem • The Future • Summary 6 Kitware • Founded in 1998 by five former GE Research employees • 118 current employees; 39 with PhDs • Privately held, profitable from creation, no debt • Rapidly Growing: >30% in 2011, 7M web-visitors/quarter • Offices • 2011 Small Business – Clifton Park, NY Administration’s Tibbetts Award – Carrboro, NC • HPCWire Readers – Santa Fe, NM and Editor’s Choice – Lyon, France • Inc’s 5000 List: 2008 to 2011 Kitware: Core Technologies CMake CDash 8 Supercomputing Visualization • Scientific Visualization • Informatics • Large Data -
Implementing the DICOM Standard for Digital Pathology
[Downloaded free from http://www.jpathinformatics.org on Tuesday, May 7, 2019, IP: 4.16.85.218] Original Article Implementing the DICOM Standard for Digital Pathology Markus D. Herrmann1, David A. Clunie2, Andriy Fedorov3,4, Sean W. Doyle1, Steven Pieper5, Veronica Klepeis4,6, Long P. Le4,6, George L. Mutter4,7, David S. Milstone4,7, Thomas J. Schultz8, Ron Kikinis3,4, Gopal K. Kotecha1, David H. Hwang4,7, Katherine P. Andriole1,4,9, A. John Iafrate4,6, James A. Brink4,10, Giles W. Boland4,9, Keith J. Dreyer1,4,10, Mark Michalski1,4,10, Jeffrey A. Golden4,7, David N. Louis4,6, Jochen K. Lennerz4,6 1MGH and BWH Center for Clinical Data Science, 3Department of Radiology, Surgical Planning Laboratory, Brigham and Women’s Hospital, 4Harvard Medical School, Departments of 6Pathology and 10Radiology, Massachusetts General Hospital, Departments of 7Pathology and 9Radiology, Brigham and Women’s Hospital, 8Enterprise Medical Imaging, Massachusetts General Hospital, Boston, MA, 5Isomics, Inc., Cambridge, MA, USA, 2PixelMed Publishing, LLC, Bangor, PA, USA Received: 30 July 2018 Accepted: 06 August 2018 Published: 02 November 2018 Abstract Background: Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. -
Kitware Source Issue 10
SOFTWARE DEVELOPER’S QUARTERLY Issue 10 • July 2009 PARAVIEW 3.6 Editor’s Note ........................................................................... 1 Kitware, Sandia National Laboratories and Los Alamos National Lab are proud to announce the release of ParaView Recent Releases ..................................................................... 1 3.6. The binaries and sources are available for download from the ParaView website. This release includes several new Why and How Apache Qpid Converted to CMake ............. 3 features along with plenty of bug fixes addressing a multi- tude of usability and stability issues including those affecting parallel volume rendering. ParaView and Python ........................................................... 6 Based on user feedback, ParaView’s Python API has under- Introducing the VisTrails Provenance Explorer Plugin for gone a major overhaul. The new simplified scripting interface makes it easier to write procedural scripts mimicking the ParaView................................................................................. 8 steps users would follow when using the GUI to perform tasks such as creating sources, applying filters, etc. Details on CDash Subprojects ............................................................... 10 the new scripting API can be found on the Paraview Wiki. We have been experimenting with adding support for Kitware News ...................................................................... 14 additional file formats such as CGNS, Silo, Tecplot using VisIt plugins. -
Implementation of the PLUS Open-Source Toolkit for Translational Research of Ultrasound-Guided Intervention Systems Release 0.2
Implementation of the PLUS open-source toolkit for translational research of ultrasound-guided intervention systems Release 0.2 Andras Lasso, Tamas Heffter, Csaba Pinter, Tamas Ungi, and Gabor Fichtinger August 15, 2012 Laboratory for Percutaneous Surgery, School of Computing, Queen’s University, Kingston, ON, Canada Abstract This document describes the design of the PLUS (Public software Library for Ultrasound) open-source toolkit. The toolkit provides a basic infrastructure for implementing ultrasound-guided intervention systems. Functionalities include collection of synchronized ultrasound and position data from a wide variety of hardware devices, spatial and temporal calibration, volume reconstruction, live streaming to end-user applications, and recording to and replay from file. Source code, documentation, tutorials, application examples are available with a BSD-type license at the project website: www.assembla.com/spaces/plus. Contents 1. Introduction 2 2. Data representation 3 3. Data acquisition 4 4. Volume reconstruction 6 5. Live data streaming 7 6. Implementation 7 7. Results 10 8. Conclusion 11 9. Acknowledgments 11 10. References 12 2 1. Introduction Ultrasound (US) has a great potential in guiding medical interventions, as it is capable of acquiring real- time images, it has low cost, small size, and does not use ionizing radiation. Positioning of interventional tools can often be successfully completed by simple free-hand manipulation of the ultrasound transducer and surgical tools. However, there is a wide range of challenging procedures that require continuous tracking of the pose (position and orientation) of the images and surgical tools are required. Research and development of position tracked (also known as navigated) ultrasound guided intervention systems requires advanced engineering infrastructure, which has not been available in the public domain, and single project-based solutions have not proven to be suitable as reusable platforms.