NiBabelNumFOCUS Sponsored Project since 2022
NiBabel is an input/output library for brain imaging, providing Python interfaces to common medical and neuroimaging file formats, including: ANALYZE, GIFTI, NIfTI1, NIfTI2, CIFTI-2, MINC1, MINC2, AFNI BRIK/HEAD, MGH, ECAT, Philips PAR/REC, and FreeSurfer geometry, annotation and morphometry files.
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Higher Education, Medical
Medical imaging, neuroscience, neuroinformatics
NiBabel is an open source library providing read/write access to common neuroimaging file formats for the Python programming language. NiBabel and Python form a workbench for neuroimaging analysis, and it is the base library for many tools implementing higher level processing.
Virtually all Python projects that interact with neuroimaging data rely on NiBabel for I/O, including Nilearn (statistical and machine learning), DIPY (computational anatomy), PyMVPA (multivariate pattern analysis), MNE (electrophysiology) and MONAI and NiftyNet (deep learning). Neuroimaging software suites such as AFNI, FSL and ANTs use NiBabel for accompanying Python modules. Additionally, because data are exposed through numpy arrays, there are many free-standing projects that apply deep learning techniques to neuroimaging data using a combination of NiBabel and a library such as Pytorch or Tensorflow.