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http://www.seg3d.org

A free volume processing segmenting tool that combines a flexible manual interface with powerful image processing and segmentation algorithms. Users can explore and label image volumes using slice windows and 3D volume rendering.

Proper citation: Seg3D (RRID:SCR_002552) Copy   

•   Source: SciCrunch Registry

https://github.com/QMICodeBase/TORTOISEV4

An integrated and flexible software package for processing of DTI data, and in general for the correction of diffusion weighted images to be used for DTI and potentially for high angular resolution diffusion imaging (HARDI) analysis. It can be run on both Linux and Mac platforms. It is composed of two modules named DIFF PREP and DIFF CALC. * DIFF_PREP - software for image resampling, motion, eddy current distortion and susceptibility induced EPI distortion corrections, and for re-orientation of data to a common space * DIFF_CALC - software for tensor fitting, error analysis, color map visualization and ROI analysis In addition, TORTOISE contains additional Utilities, such as a tool for the analysis of multi-center phantom data.

Proper citation: TORTOISE (RRID:SCR_001645) Copy   

•   Source: SciCrunch Registry

http://sig.biostr.washington.edu/projects/MindSeer/index.html

A cross-platform application for 3D brain visualization for multi-modality neuroimaging data written in Java/Java3D, that runs in both standalone and client-server mode. It supports basic data management capabilities, visualization of 3D surfaces (SPM's output or OFF files), volumes (Analyze, NIFTI or Minc) and label sets. MindSeer has 2 different modes: # Client/Server is designed to allow users to visualize data that is stored centrally and enhance collaboration. # Standalone mode is available to view local data and is built for more performance than Client/Server Both modes have the same interface and support the same features. It has a modular architecture and is designed to be extensible. Requirements: # Java 5.0 or above. # Java Web Start. # Java3D (installed automatically by Web Start).

Proper citation: MindSeer (RRID:SCR_003019) Copy   

•   Source: SciCrunch Registry

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

VUIIS (Vanderbilt University Institute of Imaging Science) Image and Data Analysis Core's data processing tools written for MATLAB and, unless stated otherwise, capable of processing 2D/3D images (matrices). These tools are written for ease of use from within MATLAB.

Proper citation: vuTools (RRID:SCR_001704) Copy   

•   Source: SciCrunch Registry

http://wiki.na-mic.org/Wiki/index.php/2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3

3D Slicer module for automated segmentation of the spine. This is an implementation of a novel model-based segmentation algorithm. This work was presented at the NA-MIC Week in Salt Lake City, Jan 2010.

Proper citation: SpineSegmentation module for 3DSlicer (RRID:SCR_002593) Copy   

•   Source: SciCrunch Registry

http://nipy.org/nitime/

Software library for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code.

Proper citation: Nitime (RRID:SCR_002504) Copy   

•   Source: SciCrunch Registry

http://www.mbfbioscience.com/neurolucida

Neurolucida is advanced scientific software for brain mapping, neuron reconstruction, anatomical mapping, and morphometry. Since its debut more than 20 years ago, Neurolucida has continued to evolve and has become the worldwide gold-standard for neuron reconstruction and 3D mapping. Neurolucida has the flexibility to handle data in many formats: using live images from digital or video cameras; stored image sets from confocal microscopes, electron microscopes, and scanning tomographic sources, or through the microscope oculars using the patented LucividTM. Neurolucida controls a motorized XYZ stage for integrated navigation through tissue sections, allowing for sophisticated analysis from many fields-of-view. Neurolucidas Serial Section Manager integrates unlimited sections into a single data file, maintaining each section in aligned 3D space for full quantitative analysis. Neurolucidas neuron tracing capabilities include 3D measurement and reconstruction of branching processes. Neurolucida also features sophisticated tools for mapping delineate and map anatomical regions for detailed morphometric analyses. Neurolucida uses advanced computer-controlled microscopy techniques to obtain accurate results and speed your work. Plug-in modules are available for confocal and MRI analysis, 3D solid modeling, and virtual slide creation. The user-friendly interface gives you rapid results, allowing you to acquire data and capture the full 3D extent of neurons and brain regions. You can reconstruct neurons or create 3D serial reconstructions directly from slides or acquired images, and Neurolucida offers full microscope control for brightfield, fluorescent, and confocal microscopes. Its added compatibility with 64-bit Microsoft Vista enables reconstructions with even larger images, image stacks, and virtual slides. Adding the Solid Modeling Module allows you to rotate and view your reconstructions in real time. Neurolucida is available in two separate versions Standard and Workstation. The Standard version enables control of microscope hardware, whereas the Workstation version is used for offline analysis away from the microscope. Neurolucida provides quantitative analysis with results presented in graphical or spreadsheet format exportable to Microsoft Excel. Overall, features include: - Tracing Neurons - Anatomical Mapping - Image Processing and Analysis Features - Editing - Morphometric Analysis - Hardware Integration - Cell Analysis - Visualization Features Sponsors: Neurolucida is supported by MBF Bioscience.

Proper citation: Neurolucida (RRID:SCR_001775) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

•   Source: SciCrunch Registry

https://neuroscienceblueprint.nih.gov/Resources-Tools/Blueprint-Resources-Tools-Library

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 22, 2023. National initiative to advance biomedical research through data sharing and online collaboration that provides data sharing infrastructure, software tools, strategies and advisory services. Groups may choose whether to share data internally or with external audiences. Hardware and data remain under control of individual user groups.

Proper citation: Biomedical Informatics Research Network (RRID:SCR_005163) Copy   

•   Source: SciCrunch Registry

Issue

https://github.com/spm

Software package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.

Proper citation: SPM (RRID:SCR_007037) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/software.cfm?ID=96

Designed for use in an MRI simulator, MoTrak software uses Ascension Technology?s Flock of Birds. The sensor attaches to the subject?s head and determines the position of the head in space relative to the transmitter. The sensor records angular rotations as well as positional displacements from an initially calibrated position. This information is displayed and logged by the program in real-time, allowing observation of head motion in an MRI simulator. In the simulator, the participant can simultaneously be habituated to the MRI environment, while being trained to remain still via feedback from the MoTrak system.

Proper citation: MoTrak Head Motion Tracking System (RRID:SCR_009607) Copy   

•   Source: SciCrunch Registry

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

Aa brain network visualization tool, which can help researchers to visualize structural and functional connectivity patterns from different levels in a quick, easy, and flexible way.

Proper citation: BrainNet Viewer (RRID:SCR_009446) Copy   

•   Source: SciCrunch Registry

http://cytoseg.googlecode.com

A tool for automatic segmentation of 3D biological datasets, with emphasis on 3D electron microscopy. It works best for 3D blob shaped objects like mitochondria, lysosomes, etc. The project is written in Python and uses the pythonxy platform (which includes scipy and ITK image processing tools).

Proper citation: Cytoseg (RRID:SCR_009553) Copy   

•   Source: SciCrunch Registry

http://www.math.mcgill.ca/keith/surfstat

A Matlab toolbox for the statistical analysis of univariate and multivariate surface data using linear mixed effects models and random field theory.

Proper citation: SurfStat (RRID:SCR_007081) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/iowa3/

Software for real-time parametric statistical analysis of functional MRI (fMRI) data. The system that combines a general architecture for sampling and time-stamping relevant information channels in fMRI (image acquisition, stimulation, subject responses, cardiac and respiratory monitors, etc.) and an efficient approach to manipulating these data, featuring incremental subsecond multiple linear regression. The advantages of the system are the simplification of event timing and efficient and unified data formatting. Substantial parametric analysis can be performed and displayed in real-time. Immediate (replay) and delayed off-line analysis can also be performed with the same interface. The system provides a time-accounting infrastructure that readily supports standard and innovative approaches to fMRI.

Proper citation: I/OWA (RRID:SCR_000858) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/DPARSF

A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.

Proper citation: DPARSF (RRID:SCR_002372) Copy   

•   Source: SciCrunch Registry

http://niftyrec.scienceontheweb.net/

Software toolbox that includes reconstruction tools for emission and transmission imaging modalities, including Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), cone-beam X-Ray CT and parallel-beam X-Ray CT. At the core of NiftyRec are efficient, GPU accelerated, projection, back-projection and iterative reconstruction algorithms. The easy to use Matlab and Python interfaces of NiftyRec enable fast prototyping and development of reconstruction algorithms. NiftyRec includes standard iterative reconstruction algorithms such as Maximum Likelihood Expectation Maximisation (MLEM), Ordered Subsets Expectation Maximisation (OSEM) and One Step Late Maximum A Posteriori Expectation Maximisation (OSL-MAPEM), for multiple imaging modalities.

Proper citation: NiftyRec (RRID:SCR_002499) Copy   

•   Source: SciCrunch Registry

http://www.warwick.ac.uk/snpm

A toolbox for Statistical Parametric Mapping (SPM) that provides an extensible framework for voxel level non-parametric permutation/randomization tests of functional Neuroimaging experiments with independent observations. SnPM uses the General Linear Model to construct pseudo t-statistic images, which are then assessed for significance using a standard non-parametric multiple comparisons procedure based on randomization/permutation testing. It is most suitable for single subject PET/SPECT analyses, or designs with low degrees of freedom available for variance estimation. In these situations the freedom to use weighted locally pooled variance estimates, or variance smoothing, makes the non-parametric approach considerably more powerful than conventional parametric approaches, as are implemented in SPM. Further, the non-parametric approach is always valid, given only minimal assumptions. The SnPM toolbox provides an alternative to the Statistics section of SPM.

Proper citation: Statistical non-Parametric Mapping (RRID:SCR_002092) Copy   

•   Source: SciCrunch Registry

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

Matlab toolbox that implements the wavelet-based image fusion technique for orthogonal images, introduced in (Aganj et al, MRM 2012).

Proper citation: Wavelet-based Image Fusion (RRID:SCR_002007) Copy   

•   Source: SciCrunch Registry

http://icatb.sourceforge.net/fusion/fusion_startup.php

A MATLAB toolbox which implements the joint Independent Component Analysis (ICA), parallel ICA and CCA with joint ICA methods. It is used to to extract the shared information across modalities like fMRI, EEG, sMRI and SNP data. * Environment: Win32 (MS Windows), Gnome, KDE * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE, NIfTI-1

Proper citation: Fusion ICA Toolbox (RRID:SCR_003494) Copy   

•   Source: SciCrunch Registry