Simpleitk Documentation Release 2.0Rc2
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Other Departments and Institutes Courses 1
Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses. -
Simpleitk Tutorial Image Processing for Mere Mortals
SimpleITK Tutorial Image processing for mere mortals Insight Software Consortium Sept 23, 2011 (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 1 / 142 This presentation is copyrighted by The Insight Software Consortium distributed under the Creative Commons by Attribution License 3.0 http://creativecommons.org/licenses/by/3.0 What this Tutorial is about Provide working knowledge of the SimpleITK platform (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 3 / 142 Program Virtual Machines Preparation (10min) Introduction (15min) Basic Tutorials I (45min) Short Break (10min) Basic Tutorials II (45min) Co↵ee Break (30min) Intermediate Tutorials (45min) Short Break (10min) Advanced Topics (40min) Wrap-up (10min) (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 4 / 142 Tutorial Goals Gentle introduction to ITK Introduce SimpleITK Provide hands-on experience Problem solving, not direction following ...but please follow directions! (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 13 / 142 ITK Overview How many are familiar with ITK? (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 14 / 142 Ever seen code like this? 1 // Setup image types. 2 typedef float InputPixelType ; 3 typedef float OutputPixelType ; 4 typedef itk ::Image<InputPixelType ,2> InputImageType ; 5 typedef itk ::Image<OutputPixelType,2> OutputImageType ; 6 // Filter type 7 typedef itk ::DiscreteGaussianImageFilter< 8 InputImageType , OutputImageType > 9 FilterType ; 10 // Create a filter 11 FilterType ::Pointer filter = FilterType ::New (); 12 // Create the pipeline 13 filter >SetInput( reader >GetOutput () ); 14 filter−>SetVariance(1.0);− 15 filter−>SetMaximumKernelWidth(5); 16 filter−>Update (); 17 OutputImageType− ::Pointer blurred = filter >GetOutput (); − (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 15 / 142 We are here to tell you that you can.. -
Book of Abstracts
Book of Abstracts June 27, 2015 1 Conference Sponsors Diamond Sponsor Platinum Sponsors Gold Sponsors Silver Sponsors Open Analytics Bronze Sponsors Media Sponsors 2 Conference program Time Tuesday Wednesday Thursday Friday 08:00 Registration opens Registration opens Registration opens Registration opens 08:30 – 09:00 Opening session (by Rector peR! M. Johansen, Aalborg University) Aalborghallen 09:00 – 10:00 Romain François Di Cook Thomas Lumley Aalborghallen Aalborghallen Aalborghallen 10:00 – 10:30 Coffee break Coffee break Coffee break (15 min) ee break Sponsored by Quantide Sponsored by Alteryx ff Session 1 Session 4 10:30 – 12:00 Sponsor session (10:15) Kaleidoscope 1 Kaleidoscope 4 Aalborghallen Aalborghallen Aalborghallen incl. co Morning Tutorials DataRobot Ecology Medicine Gæstesalen Gæstesalen RStudio Teradata Networks Regression Musiksalen Musiksalen Revolution Analytics Reproducibility Commercial Offerings alteryx Det Lille Teater Det Lille Teater TIBCO H O Interfacing Interactive graphics 2 Radiosalen Radiosalen HP 12:00 – 13:00 Sandwiches Lunch (standing buffet) Lunch (standing buffet) Break: 12:00 – 12:30 Sponsored by Sponsored by TIBCO ff Revolution Analytics Ste en Lauritzen (12:30) Aalborghallen Session 2 Session 5 13:00 – 14:30 13:30: Closing remarks Kaleidoscope 2 Kaleidoscope 5 Aalborghallen Aalborghallen 13:45: Grab ’n go lunch 14:00: Conference ends Case study Teaching 1 Gæstesalen Gæstesalen Clustering Statistical Methodology 1 Musiksalen Musiksalen ee break Data Management Machine Learning 1 ff Det Lille Teater Det Lille -
MRI-Based Radiomics Analysis for the Pretreatment Prediction Of
cancers Article MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study Renée W. Y. Granzier 1,2,* , Abdalla Ibrahim 2,3,4,5,6,† , Sergey P. Primakov 2,4,†, Sanaz Samiei 1,2,3, Thiemo J. A. van Nijnatten 3 , Maaike de Boer 2,7, Esther M. Heuts 1 , Frans-Jan Hulsmans 8, Avishek Chatterjee 2,4 , Philippe Lambin 2,3,4 , Marc B. I. Lobbes 2,3,8 , Henry C. Woodruff 2,3,4,‡ and Marjolein L. Smidt 1,3,‡ 1 Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; [email protected] (S.S.); [email protected] (E.M.H.); [email protected] (M.L.S.) 2 GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; [email protected] (A.I.); Citation: Granzier, R.W.Y.; Ibrahim, [email protected] (S.P.P.); [email protected] (M.d.B.); A.; Primakov, S.P.; Samiei, S.; van [email protected] (A.C.); [email protected] (P.L.); Nijnatten, T.J.A.; de Boer, M.; Heuts, [email protected] (M.B.I.L.); [email protected] (H.C.W.) 3 E.M.; Hulsmans, F.-J.; Chatterjee, A.; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, Lambin, P.; et al. MRI-Based 6202 AZ Maastricht, The Netherlands; [email protected] 4 The D-Lab, Department of Precision Medicine, Maastricht University, Universiteitssingel -
Cloud Computing with E-Science Applications
TERZO Information Technology • MOSSUCCA Cloud Computing with e-Science Applications The amount of data in everyday life has been exploding. This data increase has been especially signicant in scientic elds, where substantial amounts of data must be captured, communicated, aggregated, stored, and analyzed. Cloud Computing with e-Science Applications explains how cloud computing can improve data management in data-heavy elds such as bioinformatics, earth science, and computer science. Cloud Computing The book begins with an overview of cloud models supplied by the National Institute of Standards and Technology (NIST), and then: • Discusses the challenges imposed by big data on scientic data infrastructures, including security and trust issues • Covers vulnerabilities such as data theft or loss, privacy concerns, infected applications, threats in virtualization, and cross-virtual machine attack with • Describes the implementation of workows in clouds, proposing an architecture composed of two layers—platform and application • Details infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), e-Science Applications and software-as-a-service (SaaS) solutions based on public, private, and hybrid cloud computing models • Demonstrates how cloud computing aids in resource control, vertical and horizontal scalability, interoperability, and adaptive scheduling Featuring signicant contributions from research centers, universities, Cloud Computing and industries worldwide, Cloud Computing with e-Science Applications presents innovative cloud -
Raluca-Maria Sandu RESEARCHER · DATA Scientist [email protected] | Rmsandu.Github.Io | Rmsandu | Rmsandu
Raluca-Maria Sandu RESEARCHER · DATA SCiENTiST [email protected] | rmsandu.github.io | rmsandu | rmsandu About Me I am a perseverant and resourceful scientific professional who just finished their PhD Degree in Biomedical Engineering at the University of Bern, previously having graduated with an MSc in the same field, and a BSc in Control Engineering and Applied Computer Science. I am specialized in working on data science and research & development projects across various topics as evidenced by my work experience and studies. Work Experience University of Bern, ARTORG Center for Biomedical Engineering Research Bern, Switzerland DOCTORAL CANDiDATE (PHD) May 2017 ‑ Feb. 2021 • Worked with data mining, image processing & registration, radiomics & feature extraction, statistics, and machine learning algorithms in the area of CT‑guided ablation treatments for liver cancer by using Python and R programming languages. • Developed a quantitative and numerical evaluation method based on DICOM image segmentations for assessing the success of liver ablation treatments which was published in a peer‑reviewed journal. More info here. Philips Research, Personal Care and Wellness Eindhoven, The Netherlands RESEARCH AND DEVELOPMENT GRADUATE STUDENT Jul. 2016 ‑ Mar. 2017 • Designed and developed a web‑based application for image annotation using JavaScript, HTML and CSS that can be found here. • Applied image processing and machine learning algorithms in Python (SVM, PCA, Random Forests) for classification of skin surface structures. Philips Research, Personal Care and Wellness Eindhoven, The Netherlands RESEARCH AND DEVELOPMENT INTERNSHiP Apr. 2016 ‑ Jun. 2016 • Contributed with exploratory 2D image analysis in Python to quantify the effect of various diets on physical appearance at skin surface. -
GNC Khan-Academy Una Estrategia.Pdf
Revista de Pedagogía ESCUELA DE EDUCACIÓN FACULTAD DE HUMANIDADES Y EDUCACIÓN UNIVERSIDAD CENTRAL DE VENEZUELA Depósito Legal pp. 197102DF193 ISSN N° 0798-9792 Caracas, julio-diciembre 2018, vol. 39, nº 105 Publicación semestral Director-Editor Ramón Alexander Uzcátegui Pacheco (Universidad Central de Venezuela) Consejo Editor Ángel Alvarado (Universidad Central de Venezuela) Doris Villaroel (Universidad Central de Venezuela) María Janet Ríos (Universidad Central de Venezuela) Mariángeles Payer (Universidad Central de Venezuela) Rosa Leonor Junguittu (Universidad Central de Venezuela) Eduardo Cavieres Fernández (Universidad de Playa Ancha, Chile) Maria Helena Michels (Universidade Federal de Santa Catarina, Brasil) Versión electrónica de la Revista Saber UCV, Redalyc, Revencyt y Scopus Consejo Asesor Carlos Eduardo Blanco (Universidad Central de Venezuela) Luis Bravo Jáuregui (Universidad Central de Venezuela) Aurora Lacueva (Universidad Central de Venezuela) Nacarid Rodríguez (Universidad Central de Venezuela) Juan Haro (Universidad Central de Venezuela) Alexandra Mulino (Universidad Central de Venezuela) Traducciones y Correcciones Gabriela Delgado Montoya Mariel Escuela de Educación - UCV Apoyo Secretarial y Logístico Luzmelys Martínez Judith Solórzano Consejo Asesor Honorario Internacional Martha Aguirre, Universidad Católica Boliviana “Santa Cruz” Ana Lupita Chaves Salas, Universidad de Costa Rica Carlos Miñana Blasco, Universidad Nacional de Colombia Ángel Pérez Gómez, Universidad de Málaga César Coll, Universidad de Barcelona Fernando -
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. -
User! 2020 Tutorials – Afternoon Session
useR! 2020 Tutorials – Afternoon Session How green was my valley - Spatial analytics with PostgreSQL, PostGIS, R, and PL/R by J. Conway This tutorial combines the use of PostgreSQL with PostGIS and R spatial packages. It covers data ingestion including cleaning and transformation, and basic analysis, of vector and raster based geospatial data from open sources. Learning outcomes: At the end of the tutorial, the participants will: • learn the advantages and disadvantages of using PostgreSQL, PostGIS, R, and PL/R to process geospatial data; • perform a basic installation and setup of the technology stack, including PostgreSQL, PostGIS, R, and PL/R; • gain experience on the data cleaning, subsetting, and ingestion process for that data; • perform several simply types of visualization and analysis on the ingested data. Requirements: The audience should be comfortable with R, SQL, and spatial data at an intermediate level. The attendees need only a modern OS/web browser. The hands-on exercises will be done via Katacoda. Creating beautiful data visualization in R: a ggplot2 crash course by S. Tyner Learning ggplot2 brings joy and “aha moments” to new R users, keeping them more engaged and eager to grow their R skills. Newer R users will be and feel more empowered with data visualization skills. In addition to experiencing joy in creating beautiful graphics, advanced R users will learn to take advantage of ggplot2’s elegant defaults, saving time on manual plotting tasks like drawing legends. Thus, time and energy can be spent on advanced analyses, -
Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation
J Archaeol Method Theory (2017) 24:424–450 DOI 10.1007/s10816-015-9272-9 Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation Ben Marwick1,2 Published online: 7 January 2016 # Springer Science+Business Media New York 2016 Abstract The use of computers and complex software is pervasive in archaeology, yet their role in the analytical pipeline is rarely exposed for other researchers to inspect or reuse. This limits the progress of archaeology because researchers cannot easily reproduce each other’sworktoverifyorextendit.Fourgeneralprinciplesofrepro- ducible research that have emerged in other fields are presented. An archaeological case study is described that shows how each principle can be implemented using freely available software. The costs and benefits of implementing reproducible research are assessed. The primary benefit, of sharing data in particular, is increased impact via an increased number of citations. The primary cost is the additional time required to enhance reproducibility, although the exact amount is difficult to quantify. Keywords Reproducible research . Computer programming . Software engineering . Australian archaeology. Open science Introduction Archaeology, like all scientific fields, advances through rigorous tests of previously published studies. When numerous investigations are performed by different re- searchers and demonstrate similar results, we hold these results to be a reasonable approximation of a true account of past human behavior. This -
3D Slicer Documentation
3D Slicer Documentation Slicer Community Sep 25, 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.............................................. -
Quantitative Analysis of Medical Imaging Data in R
Medical Image Analysis in R Quantitative Analysis of Motivation Medical Imaging Data in R Task View Case Studies fMRI DTI PET Brandon Whitcher Opportunities Mango Solutions End London, United Kingdom www.mango-solutions.com [email protected] @MangoImaging 24 November 2011 – Neuroimaging and Statistics Medical Image Analysis in R Outline Motivation Task View Case Studies 1 Motivation fMRI DTI PET 2 Medical Imaging Task View Opportunities End 3 Case Studies Functional MRI Diffusion Tensor Imaging Positron Emission Tomography 4 Opportunities 5 Conclusions Medical Image Analysis in R The Drug Development Process Motivation Task View Case Studies fMRI DTI PET Opportunities End • New drug development can take from 10-20 years with an estimated average of about 9-12 years. • The best estimate of the costs of drug R&D today is likely to be that from the most recently available well-designed study; that is, USD 802 million. Dickson & Gagnon (2009; Discovery Medicine) Medical Image Analysis in R Medical Image Analysis for Drug Development Motivation • Quantitative image analysis and statistical inference. Task View • Application development, validation and deployment. Case Studies fMRI • Translational imaging: pre-clinical and clinical studies. DTI PET • Work with clinical scientists to determine suitable Opportunities imaging biomarkers. End • Work with medical physicists to determine appropriate image acquisition guidelines. Three stages of a clinical imaging study. • Setup • Operations • Analysis Medical Image Analysis in R The R Project for Statistical Computing Motivation • R is a free software environment for statistical Task View computing and graphics. Case Studies • R compiles and runs on a wide variety of UNIX fMRI DTI platforms, Windows and MacOS.