Reverse Dependencies of nibabel

The following projects have a declared dependency on nibabel:

• fw-gear-nifti-to-mips — Convert nifti to MIPS.
• fw-gear-rtp2-pipeline — Tract and metrics generation for Diffusion MRI
• fw-gear-rtp2-preproc — RTP2 Pre-processing Diffusion MRI
• fw-gear-validated-file-metadata-importer — Extract metadata of input file to Flywheel.
• gawseed-tcorex — Correlation Explanation Methods
• gbb — Gradient-based boundary (GBB) surface refinement
• gcam — An easy to use framework that makes model predictions more interpretable for humans.
• gcnn-dmri — Graph-equivariant CNNs for diffusion MRI
• glm-express — Automated linear models for functional neuroimaging data
• gramform — Grammar for string-to-function formulae
• greedypy — Fast deformable registration in python
• hcpre — Generalized launcher for human connectome project BOLD preprocessing
• hdi-utils — High-dimensional image data utilities
• heifetslab-unravel — UNRAVEL: UN-biased high-Resolution Analysis and Validation of Ensembles using Light sheet images
• herbs — A Python-based GUI for Histological E-data Registration in Brain Space
• heudiconv — Heuristic DICOM Converter
• hf-deepali — Image, point set, and surface registration library for PyTorch.
• highresnet — PyTorch implementation of HighRes3DNet
• hippunfold-toolbox — A toolbox for viewing, manipulating, and additional actions on HippUnfold outputs
• hipsta — A python package for hippocampal shape and thickness analysis
• Hive-MAIA — Python Package to support Deep Learning data preparation, pre-processing. training, result visualization and model deployment across different frameworks (nnUNet, nnDetection, MONAI).
• hiwenet — Histogram-weighted Networks for Feature Extraction and Advance Analysis in Neuroscience
• hmp — Package for fitting Hidden Multivariate pattern model to time-series
• horos-io — small package to deal with data exported from Horos
• hypast — Hypothalamus Automatic Segmentation Tool
• hypercoil — Differentiable programming for neuroimaging analysis
• hyve — Interactive and static 3D visualisation for functional brain mapping
• im2mesh — Python library to create Finite Element meshes from segmented image stacks
• imagedata — Read/write medical image data
• img-pipe — Image processing pipeline for localization and identification of electrodes for electrocorticography
• imio — Loading and saving of image data.
• indexed-gzip — Fast random access of gzip files in Python
• Ingress2QSIRecon — Tool to ingress data from other pipelines for use in QSIRecon
• inpainting — Official package to compute metrics for the BraTS inpainting challenge.
• inpainting-metrics — TODO.
• intensity-normalization — normalize the intensities of various MR image modalities
• interactivenet — InteractiveNet, a framework for minimally interactive medical image segmentation.
• ivadomed — Feature conditioning for IVADO medical imaging project.
• JALE — Package allowing users to run Activation Likelihood Estimation Meta-Analysis
• jarvis-md-nifti — no summary
• jem — Python package for MRI and electrophysiology analysis.
• junifer — JUelich NeuroImaging FEature extractoR
• kaiko-eva — Evaluation Framework for oncology foundation models.
• kelluwen — Open AI library for research and education.
• kymata — Core Kymata codebase, including statistical analysis and plotting tools
• labelmerge — Snakebids app for merging multiple label maps.
• lapy — A package for differential geometry on meshes (Laplace, FEM)
• lesion-metrics — metrics for evaluating lesion segmentations
• linc-convert — Linc Convert Scripts
• linumpy — linumpy: microscopy tools and utilities
• liom-toolkit — Package to support the research of LIOM.
• lionz — A toolkit for precise segmentation of tumors in PET/CT scans.
• livermask — A package for automatic segmentation of liver from CT data
• lsfmpy — HDF5-based software for storing and managing voluminous 3D imaging data
• lwviewv2 — Viewer of images in python.
• lyman — lyman: neuroimaging analysis in Python
• lytemaps — Lightweight implementation of neuromaps
• m2g — Neuro Data MRI to Graphs Pipeline
• macapype — Pipeline for anatomic processing for macaque
• mapca — Moving Average Principal Component Analysis for fMRI data
• MARSS — MARSS is a regression-based method that mitigates an artifactual shared signal between simultaneously acquired slices in unprocessed MB fMRI.
• matplotlib-surface-plotting — Brain mesh plotting in matplotlib
• mcot.cifti — CIFTI/greyordinate interface
• mcot.dippi — Simulates/fits DIPPI data
• mcot.gcoord — Gyral coordinate system
• mcot.pipe — Declerative pipeline definition
• mcot.surface — surface interface
• mcot.utils — Utilities for mcot including script interface
• mct — The MRI Coil-reconstruct Toolbox
• mdai — MD.ai Python client library
• medcam — An easy to use library that makes model predictions more interpretable for humans.
• MedicalMultitaskModeling — Multitask learning framework for medical data
• medicaltorch — An open-source pytorch medical framework.
• medimage-pkg — MEDimage is a Python package for processing and extracting features from medical images
• medimages4tests — Generates dummy medical image data with realistic headers to be used in image handling tests
• medio — Medical images I/O Python package
• medivisio — This script helps to visualise 3D medical images of type DICOM and NII
• medreaders — Readers for medical imaging datasets
• medviz — Medical Image Visualization Tool 🐍🚀🎉🦕
• meggie-sourceanalysis — no summary
• mgz2imgslices — (Python) utility to filter mgz volumes to per-voxel-value directories of jpg/png image slices
• mia-processes — mia_processes
• miaaim-python — Multi-omics Image Alignment and Analysis by Information Manifolds
• micomputing — 'micomputing' is a package for medical image computing.
• mindstorm — Mindstorm: Advanced analysis of neuroimaging data
• miniqc — A BIDS app for performing minimal QC beyond validation
• mippy — Modular Image Processing in Python
• mircat — Mirshahi CT Analysis Toolkit (MirCAT). Convert, Segment, and Quantify CT NIfTI files.
• mircat-stats — Mirshahi CT Analysis Toolkit (MirCAT), stats and dicom conversion only. Convert and Quantify CT NIfTI files.
• mircato — Add your description here
• mircato-stats — Statistics only version of MirCATo for CT image analysis. Much smaller build without torch.
• miscnn — Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
• miscnn-TF-1.14 — Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
• miscnn-TF-2.0 — Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
• MiTfAT — A Python-based Scikit-Learn-friendly fMRI Analysis Tool, Made in Tuebingen.
• miutil — Medical imaging utilities for the AMYPAD and NiftyPET projects
• ml4h — Machine Learning for Health python package
• mmsegmentation — Open MMLab Semantic Segmentation Toolbox and Benchmark
• mmsegmentation-building — Open MMLab Semantic Segmentation Toolbox and Benchmark, forked for pleiade RGB image segmentation
• mne-bids — MNE-BIDS: Organizing MEG, EEG, and iEEG data according to the BIDS specification and facilitating their analysis with MNE-Python