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J Digit Imaging. 2018 Jun;31(3):290-303. doi: 10.1007/s10278-017-0037-8.

SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research.

Author information

1
TAJ Technologies Inc., Bloomington, MN, 55425, USA. zivyaniv@nih.gov.
2
National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. zivyaniv@nih.gov.
3
National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
4
MSC LLC, Rockville, MD, 20852, USA.
5
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, 52242, USA.
6
Department of Medicine, Monash University, Melbourne, VIC, 3168, Australia.

Abstract

Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks .

KEYWORDS:

Image analysis; Open-source software; Python; R; Registration; Segmentation

PMID:
29181613
PMCID:
PMC5959828
[Available on 2019-06-01]
DOI:
10.1007/s10278-017-0037-8

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