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Generic classification tool for 3D images

Proper citation: COMPARE (RRID:SCR_000855) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.org:8080/nvm/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6, 2023. Software tool for quantitative neuroanatomical measurements in volumetric image data. Used to draw regions of interest for subsequent fMRI analysis.

Proper citation: NVM (RRID:SCR_000600) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://www.brainvisa.info/

BrainVISA is a modular an customizable software platform built to host heterogeneous tools dedicated to neuroimaging research. Many toolboxes have already been developed for BrainVISA (T1 MRI, sulcal identification and morphometry, cortical surface analysis, diffusion imaging and tractography, fMRI, nuclear imaging, EEG and MEG, TMS, histology and autoradiography, etc.). Anatomist is a software for interactive visualization of multimodal data and for manipulation of structured 3D objects. It allows to build scenes that merge or combine images, meshes, regions of interest, fibers, textures, color palettes, referential changes, etc. A user can interact in 3D and in real time with the objects of an Anatomist scene: change point of view, select objects, add/suppress objects, change colors, draw regions of interests, do manual registration, etc. BrainVISA main features are: * Harmonization of communications between different software. For instance, BrainVISA toolboxes are using home-made software but also third-party software such as FreeSurfer, FSL, SPM, nipy, R-project, Matlab, etc. * Ontology-based data organization allowing database sharing and automation of mass of data analysis. * Fusion and interactive visualization of multimodal data (using Anatomist software). * Automatic generation of graphical user interfaces. * Workflow monitoring and data quality checking. * Full customization possible. * Runs on Linux, Mac and Windows. * Programming Language: C++, Python * Supported Data Format: ANALYZE, DICOM, GIfTI, MINC, NIfTI-1, Other Format

Proper citation: BrainVISA / Anatomist (RRID:SCR_007354) Copy   

•   Source: SciCrunch Registry

http://fmri.wfubmc.edu/software/PickAtlas

A software toolbox that provides a method for generating Region of Interest (ROI) masks based on the Talairach Daemon database. The atlases include Brodmann area, Lobar, Hemisphere, Anatomic Label (gyral anatomy), and Tissue type. The atlases have been extended to the vertex in MNI space, and corrected for the precentral gyrus anomaly. Additional atlases (including non-human atlases) can be added without difficulty., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: WFU PickAtlas (RRID:SCR_007378) Copy   

•   Source: SciCrunch Registry

http://mipav.cit.nih.gov

A Java-based application that enables quantitative analysis and visualization of medical images of numerous modalities such as DTI, PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders. MIPAV can be run on any Java-enabled platform such as Windows, UNIX, or Macintosh OS X. Functionality includes segmentation, inter- and intra multi-modality registration, surface rendering, volume rendering and reading and writing a large number of biomedical file formats including: DICOM 3.0, Analyze, NIFTI, SPM, MINC, Phillips, GE, Zeiss, Biorad, jpeg, png, tiff, mrc, fits, interfile, and many more.

Proper citation: MIPAV: Medical Image Processing and Visualization (RRID:SCR_007371) Copy   

•   Source: SciCrunch Registry

http://www.alivelearn.net/xjview8/

A viewing program for Statistical Parametric Mapping (SPM2, SPM5 and SPM8). p-value slider, displays multiple images at a time and can be used to build Region of Interest (ROI) masks. For a given region you can find the anatomical name and search the selected region in online database (wiki, Google scholar and PubMed).

Proper citation: xjView: A Viewing Program For SPM (RRID:SCR_008642) Copy   

•   Source: SciCrunch Registry

https://www.humanconnectome.org/software/connectome-workbench

Software brain visualization, analysis and discovery tool for fMRI and dMRI brain imaging data, including functional and structural connectivity data generated by the Human Connectome Project. Used to map brain imaging data. Allows for visualization of outputs from HCP pipelines from single subject, or average data from group of subjects and register that data onto standard brain atlas.

Proper citation: Connectome Workbench (RRID:SCR_008750) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dots/

A fast, scalable tool developed at the Johns Hopkins University to automatically segment the major anatomical fiber tracts within the human brain from clinical quality diffusion tensor MR imaging. With an atlas-based Markov Random Field representation, DOTS directly estimates the tract probabilities, bypassing tractography and associated issues. Overlapping and crossing fibers are modeled and DOTS can also handle white matter lesions. DOTS is released as a plug-in for the MIPAV software package and as a module for the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: DOTS WM tract segmentation (RRID:SCR_009459) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/c8c8/

A small, stand-alone MatLab toolbox that measures sagittal cross-section thickness and area of the human corpus callosum from high-resolution T1 in vivo MR images. C8 takes as input affine normalized white matter segmentations derived from high-resolution (in-plane) T1 images and outputs both regional callosal thicknesses in three different formats and geometrically-defined regional areas in three different configurations. It is a small package that is easily configurable and modifiable and it measures callosa at the rate of several per minute.

Proper citation: C8: Corpus Callosum Computations (RRID:SCR_009449) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/best_practices/

A tutorial that took place as part of MICCAI 2010 is the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, September 20-24, 2010 in Beijing, China. See http://www.miccai2010.org/ This project supporedt community outreach and dialog between presenters and audience, both before and after the tutorial session, and is intended to engage the broader community in the deliberations on this important topic. ''Best Practices'' covered software engineering practices as well end-user installation and support practices.

Proper citation: Best Practices for Software Development (RRID:SCR_009441) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/btk/

Software toolkit developed for the fbrain project that consists of several image processing tools: image reconstruction, image denoising, image segmentation, tractography etc., for a better understanding of fetal brain development.

Proper citation: Baby Brain Toolkit (RRID:SCR_009440) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/groupwisereg/

An open source implementation of a non-rigid groupwise registration method. This project is implemented by Serdar K Balci (serdar at csail.mit.edu) and supervised by Polina Golland and William M. Wells All metrics are implementing in a multi-threaded fashion. The algorithm will run faster on computers with multiple CPU''s.

Proper citation: Non-rigid groupwise registration method (RRID:SCR_009512) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/books

Bibliography of books related to neuroscience addressing the topic of functional and structural neuroimaging.

Proper citation: NITRC Books (RRID:SCR_009510) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ern/

An efficient framework for building and analyzing graphs called epsilon radial networks (ERNs) using tractography data in a normalized space. Currently there is no agreed-upon method for constructing the brain anatomical connectivity graphs out of large number of white matter tracts. The key challenge in defining brain networks is node delineation and their method defines nodes in the graph using tract-end points clustered in a sphere of a given radius (epsilon). Using a kd-tree based search algorithm they can identify the nodes computationally efficiently and in a fully automatic way. These networks can be used not only to analyze topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus ERNs can be used as a novel image processing lens for statistical and machine learning based analyses.

Proper citation: Epsilon Radial Networks (RRID:SCR_009470) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/lll/

Software using a novel local label learning strategy to estimate the target image?s segmentation label using statistical machine learning techniques. They used a support vector machine (SVM) with a K nearest neighbor (KNN) based training sample selection strategy to learn a classifier for each of the target image voxel based on a training dataset consisting of its neighboring voxels in the atlases. Validation experiments on hippocampus segmentation of 117 MR images demonstrated that the method can produce segmentation results consistently better than state-of-the-art label fusion methods.

Proper citation: Local Label Learning Segmentation (RRID:SCR_009504) Copy   

•   Source: SciCrunch Registry

http://www.3dbar.org

Software package for reconstructing three-dimensional models of brain structures from 2-D delineations using a customizable and reproducible workflow. 3dBAR also works as an on-line service (http://service.3dbar.org) offering a variety of functions for the hosted datasets: * downloading reconstructions of desired brain structures in predefined quality levels in various supported formats as well as created using customizable settings, * previewing models as bitmap thumbnails and (for webGL enabled browsers) interactive manipulation (zooming, rotating, etc.) of the structures, * downloading slides from available datasets as SVG drawings. 3dBAR service can also be used by other websites or applications to enhance their functionality. * Operating System: Linux * Programming Language: Python * Supported Data Format: NIfTI-1, Other Format, VRML

Proper citation: 3DBar (RRID:SCR_008896) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/landman/

Project to provide long-term hosting and release for small tools related to medical image analysis. Source repository contains highly experimental code intended for collaborative development. However, any interested parties are welcome to browse/reuse code. Stable/evolved projects will be moved to independent projects.

Proper citation: Landman NeuroImaging Tools (RRID:SCR_009503) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/jhumipavplugins/

This repository stores plugins, tutorial code, and examples demonstrating MRI manipulation within the MIPAV plugin environment. This project is separate from JIST so that we can provide WRITE access to any interested party without overly exposing the infrastructure to unplanned modification. Please contact the administrators if you would like to join this project - open use is encouraged.

Proper citation: JIST Resources for Algorithm Development (RRID:SCR_009500) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dkfz-diffusion/

This central project points to all open-source and open-data initiatives provided by the German Cancer Research Center in the field of diffusion MRI.

Proper citation: Diffusion MRI at DKFZ Heidelberg (RRID:SCR_009465) Copy   

•   Source: SciCrunch Registry