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http://www.imagevis3d.org/

A new volume rendering program developed by the NIH/NCRR Center for Integrative Biomedical Computing (CIBC). The main design goals of ImageVis3D are: simplicity, scalability, and interactivity. Simplicity is achieved with a new user interface that gives an unprecedented level of flexibility (as shown in the images). Scalability and interactivity for ImageVis3D mean that both on a notebook computer as well as on a high end graphics workstation, the user can interactively explore terabyte sized data sets. Finally, the open source nature as well as the strict component-by-component design allow developers not only to extend ImageVis3D itself but also reuse parts of it, such as the rendering core. This rendering core, for instance, is planned to replace the volume rendering subsystems in many applications at the SCI Institute and with their collaborators.

Proper citation: ImageVis3D (RRID:SCR_009566) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/draw3d/

Draw3D is a 3D rendering tool written entirely in VTK-TCL script. It is intended for fast command line rendering and visual inspection of datasets commonly found in medical imaging. It also allows the generation of images for reports or videos. As it is based on pure VTK, it can render whatever VTK can render, and runs wherever VTK can run. Meshinator is a simpler tool that uses VTKs isosurface functions to generate meshes from volumetric data. Please see the wiki for documentation

Proper citation: Draw3D and Meshinator (RRID:SCR_009444) Copy   

•   Source: SciCrunch Registry

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

High quality data, open and freely available to everyone to celebrate the diversity of the vertebrate brain. Do you have data that you would like to share? Do not hesitate to contact them! The Brain Catalogue is developed by Florencia Grisanti (Taxidermy Workshop of the Natural History Museum in Paris) and Roberto Toro (Neuroscience Department of the Institut Pasteur). Many of our specimens come from the Vertebrate Brain Collection of the Jardin des Plantes, curated by Marc Herbin, and are scanned at the Institut du Cerveau et de la Moelle (ICM) by Mathieu Santin and Alexandra Petiet, from the CENIR laboratory, with financial and methodological support kindly provided by Olivier Colliot, head of the Cogimage team at the ICM.

Proper citation: Brain Catalogue (RRID:SCR_009442) Copy   

•   Source: SciCrunch Registry

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

Human DTI brain atlases have been generated at UNC-Chapel Hill for several age groups, by iterative joint deformable registration of training datasets into a single unbiased DTI average image. Atlases packages include an atlas DTI tensor image, atlas DTI property images (FA, MD, AD, RD), and single tensor tractography based fiber tracts of major tracts with related 3D planes for fiber profile information: genu, splenium, anterior and posterior limb of internal capsule, uncinate fasciculus.

Proper citation: UNC Human DTI Brain Atlas (RRID:SCR_009516) Copy   

•   Source: SciCrunch Registry

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

A nice sign of bias field correction (nonuniformity) in medical images. This tool is fast and efficient. Technical details can be found at http://zheng.vision.googlepages.com/biasCorrection_miccai09_Zheng.pdf

Proper citation: NICE-SIGN (RRID:SCR_009629) Copy   

•   Source: SciCrunch Registry

http://www.gtec.at/Products/Software/g.BSanalyze-Specs-Features

An interactive environment for multimodal biosignal data processing and analysis in the fields of clinical research and life sciences. It is the most comprehensive package to analyze non-invasive and invasive brain-, heart- and muscle-functions and dysfunctions. It includes many functions such as support vector machines, event-related ECG, support for P300 and SSVEP/SSSEP BCIs, zero class detection for BCIs, compressed spectral array, minimum energy, and more! g.BSanalyze consists of a base version for data import, visualization, transformation and pre-processing and has several dedicated toolboxes. The package comes with many sample biosignal data-sets, including P300, SSVEP, motor imagery, CSP BCIs, Tilt-Table, EPs, multi-unit activity, CFM, and ERD/ERS.

Proper citation: g.BSanalyze (RRID:SCR_009625) Copy   

•   Source: SciCrunch Registry

http://www.xinapse.com/

A medical image display package that allows easy viewing and analysis of Magnetic Resonance, x-ray CT and other types of medical image. Jim is an up-to-the-minute design with a familiar user-interface.

Proper citation: Jim (RRID:SCR_009589) Copy   

•   Source: SciCrunch Registry

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

Software Python tool as viewer for MRI data and numpy arrays.

Proper citation: vini: A viewer for fMRI data (RRID:SCR_017250) Copy   

•   Source: SciCrunch Registry

https://github.com/Neural-Systems-at-UIO/MeshView-for-Brain-Atlases

Web application for real time 3D display of surface mesh data representing structural parcellations and generation of user defined cut planes from volumetric atlases.

Proper citation: MeshView (RRID:SCR_017222) Copy   

•   Source: SciCrunch Registry

http://trackvis.org/dtk/

Software as set of commandline tools with GUI frontend that performs data reconstruction and fiber tracking on diffusion MR images. It does preparation work for TrackVis. Software Package for diffusion imaging data processing and tractography.

Proper citation: Diffusion Toolkit (RRID:SCR_017345) Copy   

•   Source: SciCrunch Registry

https://github.com/bheAI/MonkeyCBP_CLI

Software toolbox for connectivity based parcellation of monkey brain. Integrated pipeline realizing tractography based brain parcellation with automatic processing and massive parallel computing. Highly automated process and high throughput performance supported by GPU option makes toolbox ready to be used by research community.

Proper citation: MonkeyCBP (RRID:SCR_017640) Copy   

•   Source: SciCrunch Registry

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

Software tool as deep neural network for predicting FreeSurfer segmentations of structural MRI volumes. This tool is implemented as both Docker and Singularity containers. Used for brain parcellation and uncertainty estimation.

Proper citation: Knowing what you know (kwyk) - Bayesian Brain Parcellation (RRID:SCR_017470) Copy   

•   Source: SciCrunch Registry

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

Portal for platforms available to conduct and compete in challenges aiming to improve scientific progress. Challenges allow researchers to share their research and problems with other subject matter experts for collaborative progress.

Proper citation: Challenge Competitions Collection (RRID:SCR_015650) Copy   

•   Source: SciCrunch Registry

https://med.inria.fr/

Software tool as multi platform medical image processing and visualization software. Functionalities include 2D/3D/4D image visualization, image registration, diffusion MR processing and tractography, filtering.

Proper citation: medInria (RRID:SCR_001462) Copy   

•   Source: SciCrunch Registry

http://nilearn.github.io

A software package to facilitate the use of statistical learning on NeuroImaging data. Namely NiLearn leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Proper citation: NiLearn (RRID:SCR_001362) Copy   

•   Source: SciCrunch Registry

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

Software tool to simplify creation and management of computing environments in Neuroimaging.

Proper citation: ReproMan (RRID:SCR_018468) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/enhanced/

Dataset of 1000 characterized community-ascertained participants using state-of-the-art multiband imaging-based resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI), genetics, and a deep phenotyping protocol from a large cross-sectional sample of brain development, maturation and aging (ages 6 - 85 yrs). The Center for Magnetic Resonance Research (CMRR), University of Minnesota, provided the NKI-RS effort with the latest version of the Multiband EPI sequence (Xu et al. 2012) and associated image reconstruction algorithms, enabling the acquisition of state-of-the-art imaging datasets for this large-scale imaging effort. The enhanced NKI-RS expands upon the phenotypic protocol of the original NKI-RS and captures a broad range of behavioral and cognitive phenomenology relevant to psychiatric health and illness. The validity and value of assessments were evaluated by consulting leaders in the field of psychiatric phenotyping.

Proper citation: NKI-RS Enhanced Sample (RRID:SCR_010461) Copy   

•   Source: SciCrunch Registry

http://biodev.ece.ucsb.edu/projects/bisquik/wiki

A scalable web-based system for biological image analysis, management and exploration. The Bisque system incorporates many features useful to imaging researchers from image capture to extensible image analysis and querying. At the core, bisque maintains a flexible database of images and experimental metadata. Image analyses can be incorporated into the system and deployed on clusters and desktops. Search and comparison of datasets by image data and content is supported. Novel semantic analyses are integrated into the system allowing high level semantic queries and comparison of image content. New features and testing of Bisque version: 0.5.1, among many others are: # Parallel execution of datasets # Rich interfaces for autogenerated module UI # Abstracted storage system for local, irods, etc.. They are using Mercurial for their source control system. This should be installed before proceeding. Browse source on-line, http://biodev.ece.ucsb.edu/projects/bisquik/browser Bisque Installation, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/InstallationInstructions05 Bisque DOWNLOAD, http://biodev.ece.ucsb.edu/projects/bisquik/wiki/download, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bisque (RRID:SCR_005564) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/cs_schizbull08/

This project hosts data for CANDI Share Schizophrenia Bulletin 2008 (reference below) as part of the CANDI Neuroimaging Access Point. This set includes preprocessed MRI images and segmentation results of all 4 diagnostic groups (Healthy Controls, N=29; Schizophrenia Spectrum, N=20; Bipolar Disorder with Psychosis, N=19; and Bipolar Disorder without Psychosis, N=35). Frazier JA, Hodge SM, Breeze JL, Giuliano AJ, Terry JE, Moore CM, Kennedy DN, Lopez-Larson MP, Caviness VS, Seidman LJ, Zablotsky B, Makris N. Diagnostic and sex effects on limbic volumes in early-onset bipolar disorder and schizophrenia. Schizophr Bull. 2008 Jan;34(1):37-46.

Proper citation: CANDI Share: Schizophrenia Bulletin 2008 (RRID:SCR_009451) Copy   

•   Source: SciCrunch Registry

http://www.radiologyresearch.org/HippocampusSegmentation.aspx

This dataset contains T1-weighted MR images of 50 subjects, 40 of whom are patients with temporal lobe epilepsy and 10 are nonepileptic subjects. Hippocampus labels are provided for 25 subjects for training. The users may submit their segmentation outcomes for the remaining 25 testing images to get a table of segmentation metrics.

Proper citation: MRI Dataset for Hippocampus Segmentation (RRID:SCR_009597) Copy   

•   Source: SciCrunch Registry