The Insight ToolKit image registration framework

By Brian B. Avants, Nicholas J. Tustison, James C. Gee, Gang Song, Baohua Wu, Michael Stauffer. Front. Neuroinform., 2014

644 citations recorded by the source corpus.

Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK4) seeks to establish new standards in publicly available image registration methodology. ITK4 makes several advances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.1

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/f5975baa6c0d6d35dabffe7c6d190f589602c62a

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In Computer Vision and Pattern Recognition

Citations in the corpus

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “y. The Statistical Parametric Mapping (SPM) software package7 and Advanced Normalization Tools8 (ANTs) also offer solutions for both linear and non-linear registration ( Ashburner and Friston, 2000; Avants et al., 2014; Friston et al., 1995). 1 http://surfer.nmr.mgh.harvard.edu/fswiki/recon-all 2 ht”
MRI Augmentation via Elastic Registration for Brain Lesions Segmentation (2017, methodology): “Various tools were developed and implemented to normalize images, reduce variability and remove irrelevant information from MRI (ANTs [2], FSL [11] to name a few).”
SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research (2017, methodology): “Since then, the toolkit has been significantly extended to include over 280 ITK filters, with the primary addition being the incorporation of the ITK version 4 (ITKv4) registration framework [3].”
SCT: Spinal Cord Toolbox, an open‐source software for processing spinal cord MRI data (2017, methodology): “The only external dependent software is ANTs (Avants et al., 2014), however the required binaries are already included in SCT packages therefore users don’t need to install it.”