fsleyes_props is a framework for event-driven programming using Python descriptors, similar in functionality to, and influenced by Enthought Traits.

heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.

The Insight Toolkit (ITK) is a toolkit for N-dimensional scientific image processing, segmentation, and registration. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

MRIQC extracts no-reference image quality metrics from structural (T1w and T2w), functional and diffusion MRI data.

Connectome Workbench is a visualization and discovery tool used to map neuroimaging data, especially data generated by the Human Connectome Project. It allows exploration of data and activity on the surface, as well as in the volume of the brain.

niworkflows is capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM).

This package provides the version schemes used for packaging software from the NiPreps organization.

This package provides processing pipelines for structural MRI.

This package provides the Python Client code for accessing neuroimaging templates hosted using TemplateFlow.

The indexed_gzip project is a Python extension which aims to provide a drop-in replacement for the built-in Python gzip.GzipFile class, the IndexedGzipFile. indexed_gzip was written to allow fast random access of compressed NIFTI image files (for which GZIP is the de-facto compression standard), but will work with any GZIP file.

The PETPVC toolbox comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data. Eight core PVC techniques are available, and those core methods can be combined to create a total of 22 different PVC techniques.

NIPY provides a platform-independent Python environment for the analysis of functional brain imaging data.

Gifticlib is a a library for reading and writing files in GIfTI format. GIfTI is a standard for Geometry Data Format for Exchange of Surface-Based Brain Mapping Data.

Nilearn enables approachable and versatile analyses of brain volumes and surfaces. It provides statistical and machine-learning tools, with instructive documentation & open community.

elastix is an image registration toolbox based on ITK. The software consists of a collection of algorithms that are commonly used to perform (medical) image registration: the task of finding a spatial transformation, mapping one image (the fixed image) to another (the moving image), by optimizing relevant image similarity metrics. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. A command-line interface enables automated processing of large numbers of data sets, by means of scripting.

migas (mee-gahs) is a Python client to facilitate communication with a migas server.

NiFreeze is a flexible framework for volume-to-volume motion estimation and correction in d/fMRI and PET, and eddy-current-derived distortion estimation in dMRI.

NeuroImaging Workflows provides processing tools for magnetic resonance images of the brain.

DIPY is the paragon 3D/4D+ medical imaging library in Python. It contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the NLP and IR communities.

The EDS-Pseudo project aims at detecting identifying entities in clinical documents, and was primarily tested on clinical reports at AP-HP's clinical data warehouse. The model is built on top of edsnlp, and consists in a hybrid model (rule-based + deep learning) for which we provide rules (eds-pseudo/pipes) and a training recipe. We also provide some fictitious templates and a script to generate a synthetic dataset.

Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.

This package provides tools for unsupervised and semi-supervised morphological segmentation.

This package provides Python bindings for the simstring text similarity matching library.