The Design of SimpleITK

By Bradley C. Lowekamp, David T. Chen, Luis Ibáñez, Daniel J. Blezek. Front. Neuroinform., 2013

765 citations recorded by the source corpus.

SimpleITK is a new interface to the Insight Segmentation and Registration Toolkit (ITK) designed to facilitate rapid prototyping, education and scientific activities via high level programming languages. ITK is a templated C++ library of image processing algorithms and frameworks for biomedical and other applications, and it was designed to be generic, flexible and extensible. Initially, ITK provided a direct wrapping interface to languages such as Python and Tcl through the WrapITK system. Unlike WrapITK, which exposed ITK's complex templated interface, SimpleITK was designed to provide an easy to use and simplified interface to ITK's algorithms. It includes procedural methods, hides ITK's demand driven pipeline, and provides a template-less layer. Also SimpleITK provides practical conveniences such as binary distribution packages and overloaded operators. Our user-friendly design goals dictated a departure from the direct interface wrapping approach of WrapITK, toward a new facade class structure that only exposes the required functionality, hiding ITK's extensive template use. Internally SimpleITK utilizes a manual description of each filter with code-generation and advanced C++ meta-programming to provide the higher-level interface, bringing the capabilities of ITK to a wider audience. SimpleITK is licensed as open source software library under the Apache License Version 2.0 and more information about downloading it can be found at http://www.simpleitk.org.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/63e8e0bdf3a662bccf3f345a6b9caa606130c0ad

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Citations in the corpus

A review on deep learning in medical image analysis (2021, methodology): “There are many freely available tools [19] and toolkits for medical image registration, such as ANTs [24] and Simple ITK [25].”
SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research (2017, methodology): “SimpleITK, originally introduced in [18], is a simplified programming interface to the algorithms and data structures of the Insight Segmentation and Registration Toolkit.”
3D Deep Learning on Medical Images: A Review (2020, methodology): “There are several freely available medical image registration software and toolkits such as SimpleITK [106] and ANTs [107].”
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020, methodology): “5 b), but they are implemented using medical imagng libraries [14,15] to take into account specific properties of medcal images, namely their size, resolution, location, and orientation see Section 1.”
SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration (2016, methodology): “The SimpleITK project [21] was developed specifically with usability in mind to overcome the steep learning curve of previous ITK wrapping efforts like WrapITK for Python, Java and Tcl [2] and ManagedITK for the .”