Abstract
Goncalves et al., (2021). NiTransforms: A Python tool to read, represent, manipulate, and apply $n$-dimensional spatial transforms. Journal of Open Source Software, 6(65), 3459, https://doi.org/10.21105/joss.03459
Highlights
Spatial transforms formalize mappings between coordinates of objects in biomedical images
Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images
The proliferation of image registration software implementations has resulted in a disparate collection of structures and file formats used to preserve and communicate the transformation
Summary
Spatial transforms formalize mappings between coordinates of objects in biomedical images. The proliferation of image registration software implementations has resulted in a disparate collection of structures and file formats used to preserve and communicate the transformation. This assortment of formats presents the challenge of compatibility between tools and endangers the reproducibility of results. Some tools are available that permit some conversions between formats, either within neuroimaging packages or standalone such as Convert3D (Yushkevich, n.d.) They are typically limited either in compatible packages and/or application coverage (e.g., only linear transforms).
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