Literature DB >> 29325029

Neuroconductor: an R platform for medical imaging analysis.

John Muschelli1, Adrian Gherman1, Jean-Philippe Fortin2, Brian Avants2, Brandon Whitcher3, Jonathan D Clayden4, Brian S Caffo1, Ciprian M Crainiceanu1.   

Abstract

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.
© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  Bioinformatics; Image analysis; Statistical modelling

Mesh:

Year:  2019        PMID: 29325029      PMCID: PMC6409417          DOI: 10.1093/biostatistics/kxx068

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  46 in total

Review 1.  Diffusion tensor imaging: concepts and applications.

Authors:  D Le Bihan; J F Mangin; C Poupon; C A Clark; S Pappata; N Molko; H Chabriat
Journal:  J Magn Reson Imaging       Date:  2001-04       Impact factor: 4.813

2.  Using control genes to correct for unwanted variation in microarray data.

Authors:  Johann A Gagnon-Bartsch; Terence P Speed
Journal:  Biostatistics       Date:  2011-11-17       Impact factor: 5.899

3.  Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Volker J Schmid; Brandon Whitcher; Anwar R Padhani; N Jane Taylor; Guang-Zhong Yang
Journal:  IEEE Trans Med Imaging       Date:  2006-12       Impact factor: 10.048

4.  N4ITK: improved N3 bias correction.

Authors:  Nicholas J Tustison; Brian B Avants; Philip A Cook; Yuanjie Zheng; Alexander Egan; Paul A Yushkevich; James C Gee
Journal:  IEEE Trans Med Imaging       Date:  2010-04-08       Impact factor: 10.048

5.  Multi-atlas segmentation with joint label fusion and corrective learning-an open source implementation.

Authors:  Hongzhi Wang; Paul A Yushkevich
Journal:  Front Neuroinform       Date:  2013-11-22       Impact factor: 4.081

6.  A reproducible evaluation of ANTs similarity metric performance in brain image registration.

Authors:  Brian B Avants; Nicholas J Tustison; Gang Song; Philip A Cook; Arno Klein; James C Gee
Journal:  Neuroimage       Date:  2010-09-17       Impact factor: 6.556

Review 7.  An introduction to diffusion tensor image analysis.

Authors:  Lauren J O'Donnell; Carl-Fredrik Westin
Journal:  Neurosurg Clin N Am       Date:  2011-04       Impact factor: 2.509

8.  Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

Authors:  B B Avants; C L Epstein; M Grossman; J C Gee
Journal:  Med Image Anal       Date:  2007-06-23       Impact factor: 8.545

9.  fslr: Connecting the FSL Software with R.

Authors:  John Muschelli; Elizabeth Sweeney; Martin Lindquist; Ciprian Crainiceanu
Journal:  R J       Date:  2015-06       Impact factor: 3.984

10.  The Insight ToolKit image registration framework.

Authors:  Brian B Avants; Nicholas J Tustison; Michael Stauffer; Gang Song; Baohua Wu; James C Gee
Journal:  Front Neuroinform       Date:  2014-04-28       Impact factor: 4.081

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  15 in total

1.  A practical guide to big data.

Authors:  Ekaterina Smirnova; Andrada Ivanescu; Jiawei Bai; Ciprian M Crainiceanu
Journal:  Stat Probab Lett       Date:  2018-03-01       Impact factor: 0.870

2.  QDECR: A Flexible, Extensible Vertex-Wise Analysis Framework in R.

Authors:  Sander Lamballais; Ryan L Muetzel
Journal:  Front Neuroinform       Date:  2021-04-22       Impact factor: 4.081

3.  A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis.

Authors:  Alessandra M Valcarcel; Kristin A Linn; Fariha Khalid; Simon N Vandekar; Shahamat Tauhid; Theodore D Satterthwaite; John Muschelli; Melissa Lynne Martin; Rohit Bakshi; Russell T Shinohara
Journal:  Neuroimage Clin       Date:  2018-10-16       Impact factor: 4.881

4.  gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2.

Authors:  Jesper L V Maag
Journal:  F1000Res       Date:  2018-09-28

5.  Recommendations for Processing Head CT Data.

Authors:  John Muschelli
Journal:  Front Neuroinform       Date:  2019-09-04       Impact factor: 4.081

6.  An improved algorithm of white matter hyperintensity detection in elderly adults.

Authors:  T Ding; A D Cohen; E E O'Connor; H T Karim; A Crainiceanu; J Muschelli; O Lopez; W E Klunk; H J Aizenstein; R Krafty; C M Crainiceanu; D L Tudorascu
Journal:  Neuroimage Clin       Date:  2019-12-27       Impact factor: 4.881

7.  Similarity-driven multi-view embeddings from high-dimensional biomedical data.

Authors:  Brian B Avants; Nicholas J Tustison; James R Stone
Journal:  Nat Comput Sci       Date:  2021-02-22

8.  Replicability, Repeatability, and Long-term Reproducibility of Cerebellar Morphometry.

Authors:  Peter Sörös; Louise Wölk; Carsten Bantel; Anja Bräuer; Frank Klawonn; Karsten Witt
Journal:  Cerebellum       Date:  2021-01-09       Impact factor: 3.847

9.  Rxnat: An Open-Source R Package for XNAT-Based Repositories.

Authors:  Adrian Gherman; John Muschelli; Brian Caffo; Ciprian Crainiceanu
Journal:  Front Neuroinform       Date:  2020-11-09       Impact factor: 4.081

10.  Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients.

Authors:  Amandine Crombé; Michèle Kind; David Fadli; François Le Loarer; Antoine Italiano; Xavier Buy; Olivier Saut
Journal:  Sci Rep       Date:  2020-09-23       Impact factor: 4.379

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