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http://slicedrop.com

A viewer for medical imaging data that supports a variety of scientific file formats out-of-the-box (see https://github.com/xtk/X/wiki/X:Fileformats for a complete list). We think that the best way to render your files is without any necessary conversions. Just drop'em on a website and they are ready to render. Just drag'n'drop some medical imaging files on this website or try one of the four examples in the right corner. Then, play with the panels on the left and click, drag and rotate the 3d content. Slice:Drop uses WebGL and HTML5 Canvas to render the data in 2D and 3D. We use our own open-source toolkit to perform the rendering, called XTK ( http://goxtk.com ).

Proper citation: Slice:Drop (RRID:SCR_002557) Copy   

•   Source: SciCrunch Registry

http://brainmap.org/sleuth/

Software application that searches the BrainMap Database for papers of interest, reads their corresponding meta-data, and plots their results as coordinates on a standard glass brain in Talairach space.

Proper citation: Sleuth (RRID:SCR_002555) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/hammer

Software for both groupwise registration and longitudinal registration, which are the necessary steps for many brain-related applications. Specifically, groupwise registration is important for unbiased analysis of a large set of MR brain images. Therefore, in this software package, they have included two of their recently-developed groupwise registration algorithms: 1) Improved unbiased groupwise registration guided with the sharp group-mean image, and 2) Hierarchical feature-based groupwise registration with implicit template (Groupwise-HAMMER for short). On the other hand, they also included their recently-developed groupwise longitudinal registration algorithm that aligns not only the longitudinal image sequence for each subject, but also align all longitudinal image sequences of all subjects to the common space simultaneously.

Proper citation: GLIRT (RRID:SCR_002390) Copy   

•   Source: SciCrunch Registry

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

Training material for the MS lesion segmentation challenge 2008 to compare different algorithms to segment the MS lesions from brain MRI scans. Data used for the workshop is composed of 54 brain MRI images and represents a range of patients and pathology which was acquired from Children's Hospital Boston and University of North Carolian. Data has initially been randomized into three groups: 20 training MRI images, 24 testing images for the qualifying and 8 for the onsite contest at the 2008 workshop. The downloadable online database consists now of the training images (including reference segmentations) and all the 32 combined testing images (without segmentations). The naming has not been changed in comparison to the workshop compeition in order to allow easy comparison between the workshop papers and the online database papers. One dataset has been removed (UNC_test1_Case02) due to considerable motion present only in its T2 image (without motion artifacts in T1 and FLAIR). Such a dataset unfairly penalizes methods that use T2 images versus methods that don't use the T2 image. Currently all cases have been segmented by expert raters at each institution. They have significant intersite variablility in segmentation. MS lesion MRI image data for this competition was acquired seperately by Children's Hospital Boston and University of North Carolina. UNC cases were acquired on Siemens 3T Allegra MRI scanner with slice thickness of 1mm and in-plane resolution of 0.5mm. To ease the segmentation process all data has been rigidly registered to a common reference frame and resliced to isotrophic voxel spacing using b-spline based interpolation. Pre-processed data is stored in NRRD format containing an ASCII readable header and a separate uncompressed raw image data file. This format is ITK compatible. If you want to join the competition, you can download data set from links here, and submit your segmentation results at http://www.ia.unc.edu/MSseg after registering your team. They require team name, password, and email address for future contact. Once experiment is completed, you can submit the segmentation data in a zip file format. Please refer submission page for uploading data format.

Proper citation: MS lesion segmentation challenge 2008 (RRID:SCR_002425) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSFit

A program for registering images with with mutual information based metric. Several registration options are given for 3,6, 9,12,16 parameter (i.e. translate, rigid, scale, scale/skew, full affine) based constraints for the registration. The program uses the Slicer3 execution model framework to define the command line arguments and can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3

Proper citation: BRAINSFit (RRID:SCR_002340) Copy   

•   Source: SciCrunch Registry

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

Software toolbox for statistical image clustering, peak detection and data extraction developed to allow the user to have flexibility of clustering their data. Based on your threshold, it will cluster your data and find the peaks within each cluster. Additionally, it has been combined with a data extraction tool that allows one to extract the data from all the scans of the analysis from all the clusters, along with several other extraction options, with a single command.

Proper citation: peak nii (RRID:SCR_002572) Copy   

•   Source: SciCrunch Registry

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

An advanced viewer for electrophysiological and behavioral data: it can display local field potentials (EEG), neuronal spikes, behavioral events, as well as the position of the animal in the environment. It also features limited editing capabilities.

Proper citation: NeuroScope (RRID:SCR_002455) Copy   

•   Source: SciCrunch Registry

http://www.vaa3d.org

A handy, fast, and versatile 3D/4D/5D Image Visualization & Analysis System for Bioimages & Surface Objects. Vaa3D is a cross-platform (Mac, Linux, and Windows) tool for visualizing large-scale (gigabytes, and 64-bit data) 3D/4D/5D image stacks and various surface data. It is also a container of powerful modules for 3D image analysis (cell segmentation, neuron tracing, brain registration, annotation, quantitative measurement and statistics, etc) and data management. Vaa3D is very easy to be extended via a powerful plugin interface. For example, many ITK tools are being converted to Vaa3D Plugins. Vaa3D-Neuron is built upon Vaa3D to make 3D neuron reconstruction much easier. In a recent Nature Biotechnology paper (2010, 28(4), pp.348-353) about Vaa3D and Vaa3D-Neuron, an order of magnitude of performance improvement (both reconstruction accuracy and speed) was achieved compared to other tools.

Proper citation: Vaa3D (RRID:SCR_002609) Copy   

•   Source: SciCrunch Registry

http://www.tractor-mri.org.uk/

Software application that includes R packages for reading, writing and visualising magnetic resonance images stored in Analyze, NIfTI and DICOM file formats (DICOM support is read only). It also contains functions specifically designed for working with diffusion MRI and tractography, including a standard implementation of the neighbourhood tractography approach to white matter tract segmentation. A shell script is also provided to run experiments with TractoR without interacting with R.

Proper citation: TractoR: Tractography with R (RRID:SCR_002602) Copy   

•   Source: SciCrunch Registry

http://tipl.labsolver.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. A lightweight C++ template library designed mainly for medical imaging processing. The design paradigm follows generic programming, and the purpose is to provide an easy-to-use and also ready-to-use library. The code is template-based, and only header files are needed to be included to the source code. This library provides the following functions: # DICOM (r), Analyze(r), Nifti (r/w), and MATLAB MAT V4 (r/w) # numerical: add, multiply, gradient. # interpolation: linear, gaussian radial basis # filters: mean, gaussian, laplacian, sobel, anisotropic diffusion # morphological processing: erosion, expansion, opening, closing # template-based Fourier transform # linear coregistration: rigid body, affine transform, least square fit, mutual information # nonlinear coregistration: The Large Deformation Diffeomorphic Metric Mapping (LDDMM)

Proper citation: Template Image Processing Library (RRID:SCR_002600) Copy   

•   Source: SciCrunch Registry

http://www.turtleseg.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. An interactive segmentation tool originally designed for 3D medical images. Accurate and automatic 3D medical image segmentation remains an elusive goal and manual intervention is often unavoidable. TurtleSeg implements techniques that allow the user to provide intuitive yet minimal interaction for guiding the 3D segmentation process.

Proper citation: TurtleSeg (RRID:SCR_002605) Copy   

•   Source: SciCrunch Registry

http://www.bic.mni.mcgill.ca/software/N3/

The perl script nu_correct implements a novel approach to correcting for intensity non-uniformity in MR data that achieves high performance without requiring supervision. By making relatively few assumptions about the data, the method can be applied at an early stage in an automated data analysis, before a tissue intensity or geometric model is available. Described as Non-parametric Non-uniform intensity Normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. Preprocessing of MR data using N3 has been shown to substantially improve the accuracy of anatomical analysis techniques such as tissue classification and cortical surface extraction.

Proper citation: MNI N3 (RRID:SCR_002484) Copy   

•   Source: SciCrunch Registry

https://github.com/gbook/nidb

Neuroimaging database designed to allow simple importing, searching, and sharing of imaging data. NIDB also provides automated pipelining with importing of results back into NIDB which can be searched along with imaging meta data.

Proper citation: NIDB - Neuroinformatics Database (RRID:SCR_002488) Copy   

•   Source: SciCrunch Registry

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

Software algorithm that automatically estimates and removes physiological noise in BOLD fMRI data, including the effects of heartbeat and respiration. This algorithm (1) masks out high-variance CSF and vascular tracts that may otherwise confound analyses, and (2) regresses out noise timeseries in grey matter tissue, using an adaptive multivariate component decomposition (Canonical Autocorrelations Analysis). PHYCAA+ is an efficient, automated procedure that does NOT require external measures of physiology, nor does it require the user to manually identify noise components. Based on the peer-reviewed article: Churchill & Strother (2013). PHYCAA+: An Optimized, Adaptive Procedure for Measuring and Controlling Physiological Noise in BOLD fMRI. NeuroImage 82: 306-325

Proper citation: PHYCAA+: adaptive physiological noise correction for BOLD fMRI (RRID:SCR_002514) Copy   

•   Source: SciCrunch Registry

https://pdbp.ninds.nih.gov

Common data management resource and web portal to promote discovery of Parkinson's Disease diagnostic and progression biomarker candidates for early detection and measurement of disease progression. PDBP will serve as multi-faceted platform for integrating existing biomarker efforts, standardizing data collection and management across these efforts, accelerating discovery of new biomarkers, and fostering and expanding collaborative opportunities for all stakeholders.

Proper citation: Parkinson’s Disease Biomarkers Program Data Management Resource (PDBP DMR) (RRID:SCR_002517) Copy   

•   Source: SciCrunch Registry

http://idealab.ucdavis.edu/software/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. A collection of software tools used for processing and organizing MRI data. The Dicom Importer allows you to to view, assemble, and organize dicom files. Subject Library is a filesystem-based search and reporting tool that can be configured to work with many different organization schemes. This package also contains a python library that can be used to write scripts for custom tasks.

Proper citation: Subject Library (RRID:SCR_002595) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/panda-tool/

Software matlab toolbox for pipeline processing of diffusion MRI images. For each subject, PANDA can provide outputs in 2 types: i) diffusion parameter data that is ready for statistical analysis; ii) brain anatomical networks constructed by using diffusion tractography. Particularly, there are 3 types of resultant diffusion parameter data: WM atlas-level, voxel-level and TBSS-level. The brain network generated by PANDA has various edge definitions, e.g. fiber number, length, or FA-weighted. The key advantages of PANDA are as follows: # fully-automatic processing from raw DICOM/NIFTI to final outputs; # Supporting both sequential and parallel computation. The parallel environment can be a single desktop with multiple-cores or a computing cluster with a SGE system; # A very friendly GUI (graphical user interface).

Proper citation: PANDA (RRID:SCR_002511) Copy   

•   Source: SciCrunch Registry

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

A set of command line tools allowing 2D and 3D image registration, mainly for medical imaging (although also relevant to other image registration problems).

Proper citation: TAPIR (RRID:SCR_002596) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/libra-longitudinal-infant-brain-processing-package

A toolbox with graphical user interfaces for processing infant brain MR images. Longitudinal (or single-time-point) multimodality (including T1, T2, and FA) (or single-modality) data can be processed using the toolbox. Main functions of the software (step by step) include image preprocessing, brain extraction, tissue segmentation and brain labeling. Linux operating system (64 bit) is required. A workstation or server with memory >8G is recommended for processing many images simutaneously. The graphical user interfaces and overall framework of the software are implemented in MATLAB. The image processing functions are implemented with the combination of C/C++, MATLAB, Perl and Shell languages. Parallelization technologies are used in the software to speed up image processing.

Proper citation: iBEAT (RRID:SCR_002470) Copy   

•   Source: SciCrunch Registry

http://sve.bmap.ucla.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. An automated online framework for performing validation studies of skull-stripping methods. Registered users may download 40 T1 MRI volumes, skull-strip them with the algorithm of their choice, and upload their segmentation results to the SVE website. The server will then compare the 40 skull-stripped results against a set of manually generated brain masks. The server computes a series of measures for the uploaded data, including Jaccard and Dice measures. It also produces images for visualizing the spatial location of the segmentation errors relative to a common space. The results are archived on the server, and the measures are viewable by visitors to the site.

Proper citation: Segmentation Validation Engine (RRID:SCR_002591) Copy   

•   Source: SciCrunch Registry