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The modules in the framework support different tasks in the segmentation realization in 3DSlicer. A module called Level-set label map evolver was developed, which takes an initial label image and a feature image as input and performs a Geodesic Active Contours evolution on the label image according to the feature image and to a different terms in the level-set equation. The evolution takes place for a customizable number of iterations. The output is a label image that can be used to produce a model. Other modules were developed to accompany the main module as can be seen in http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Level-Set_Segmentation_Framework-Documentation
Proper citation: Level-set Segmentation for Slicer3 (RRID:SCR_009558) Copy
http://www.rad.upenn.edu/sbia/software/dramms/
A software designed for deformable 2D-to-2D and 3D-to-3D image registration. Some typical applications of DRAMMS include, ** Cross-subject registration of the same organ (can be brain, breast, cardiac, etc); ** Mono- and Multi-modality registration (MRI, CT, histology); Longitudinal registration (pediatric brain growth, cancer development, etc); ** Registration under partial missing correspondences (small lesions, tumors, histological cuts). DRAMMS is implemented as a Unix command-line tool. It is fully automatic and easy to use ? users input two images, and DRAMMS will output the registered image and deformation. No need for pre-segmentation of any structures, no need for any prior knowledge, and no need for human initialization or intervention.
Proper citation: DRAMMS (RRID:SCR_009555) Copy
http://www.connectomeviewer.org/viewer/
A free, open source, cross-platform Python-based software application for visualization and analysis in connectome research. Features of the software include: Connectome File Format including metadata, networks, surfaces, volumes, track files; complex network analysis toolboxes; modular plugin architecture for extensibility; Mayavi2 for 3D Scientific Visualization and Plotting; interactive data manipulation and scripting capabilities; and Neuroimaging and Diffusion in Python libraries.
Proper citation: Connectome Viewer (RRID:SCR_009552) Copy
Matlab based cross platform software package for computation, display, and analysis of functional connectivity in fMRI (fcMRI). Used for resting state data (rsfMRI) as well as task related designs. Covers pipeline from raw fMRI data to hypothesis testing.
Proper citation: CONN (RRID:SCR_009550) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented August 23, 2017.
A web based central repository for individual and group analysis of Arterial Spin Labeling (ASL) data sets and ASL pulse sequences developed at CMFRI UCSD for MRI researchers. This resource currently hosts more 1300 ASL data sets from 22 projects and consists of mainly two main tools 1) The Cerebral Blood Flow Database and Analysis Pipeline (CBFDAP) is a web enabled data and workflow management system extended from the HID codebase on NITRC specialized for Arterial Spin Labeling data management and analysis (including group analysis) in a centralized manner. 2) Pulse Sequence Distribution System (PSDS) for managing dissamination of ASL pulse sequences developed at the UCSD CFMRI. This resource also includes web and video tutorials for end users.
Proper citation: CBFBIRN (RRID:SCR_009543) Copy
http://www.nitrc.org/projects/multixplore/
Graphical user interface that has been implemented as a 3D Slicer plugin (scripted module). It serves to display a corresponding set of cortical regions from functional connectivity matrix in an explorable 3D scene that represents brain anatomical environment. In addition to grey matter regions, MultiXplore automatically finds and extracts deterministic fiber bundles which exist between selected region(s) and adds them to the 3D environment. This feature helps in generating region-based fiber bundles given a desired whole-brain tractography data.
Proper citation: MultiXplore (RRID:SCR_014814) Copy
https://www.nitrc.org/projects/atpp
Integrated pipeline for tractography-based brain parcellation with automatic processing and massive parallel computing. ATPP offers a CLI version for parcellating multiple brain regions and a GUI version for parcellating a specific brain region. " ATPP completely follows the scientific cultural shift to open science, which aims at making scientific research including journal papers, lab notes, data, and, of course, workflow tools, accessible and transparent to all levels of society. ATPP is publicly accessible in Neuroimaging Informatics Tools and Resources Clearinghouse8 (NITRC) (https://www.nitrc.org/projects/atpp). Its source codes are hosted in GitHub9 (https://github.com/haililihai/ATPP_CLI; https://github.com/haililihai/ATPP_GUI), under the GNU generic purpose license version 310 (GPLv3), and are welcome to download and fork. The Digital Object Identifiers (DOIs) providing a persistent way to make digital data easily and uniquely citable was created from Zenodo11 platform with those GitHub repositories (ATPP CLI v2.0.0, doi: https://doi.org/10.5281/zenodo.239702; ATPP GUI v2.0.0, doi: https://doi.org/10.5281/zenodo.239705). "
Proper citation: Automatic Tractography-based Parcellation Pipeline (RRID:SCR_014815) Copy
https://github.com/BlueBrain/BluePyOpt
An extensible framework for data-driven model parameter optimization that wraps and standardizes several existing open-source tools. BluePyOpt abstracts the optimization and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. It also provides methods for setting up both small- and large-scale optimizations on a variety of platforms.
Proper citation: BluePyOpt (RRID:SCR_014753) Copy
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
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
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
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
http://www.sci.utah.edu/cibc/software/map3d.html
A scientific visualization application written to display and edit complex, three-dimensional geometric models and scalar, time-based data associated with those models such as high resolution EEG, MEG, and ECG.
Proper citation: map3d (RRID:SCR_009628) Copy
http://www.nitrc.org/projects/fmri_grocer
Software toolbox containing many kinds of kits that you may be interested in during fMRI data analysis. This toolbox is a homebrew kits built during practical ASL(arterial spin labeling) based Cerebral Blood Flow (CBF) data analysis. Meanwhile, this toolbox is also compatible with BOLD data analysis. Everyone would find something useful for their own data analysis! This toolbox is run and tested on SPM8 with MATLAB 7.6.0(R2008a) under the Linux OS. Theoretically, most of the functions (except the menu1&2 which are specially designed for the Batch Editor of SPM8) of this toolbox should be compatible with SPM5 and should also work smoothly under the Windows OS. Feel free to give feedback to authors if you encounter any bugs or problems. Senhua Zhu Center for functional Neuroimaging, University of Pennsylvania 3 W.Gates Bldg, 3400, Philadelphia, PA (19104), United States Email: [email protected] ; [email protected] QQ group number (QQ?): 60524357 Google group: https://groups.google.com/d/forum/fmri-grocer
Proper citation: fMRI Grocer (RRID:SCR_009622) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023. Software toolbox for rigid and nonrigid registration of images. elastix is open source software, based on the well-known Insight Segmentation and Registration Toolkit (ITK). The software consists of a collection of algorithms that are commonly used to solve (medical) image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. A command-line interface enables automated processing of large numbers of data sets, by means of scripting. A paper describing elastix contains more details: S. Klein, M. Staring, K. Murphy, M.A. Viergever, J.P.W. Pluim, elastix: a toolbox for intensity based medical image registration,; IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 196 - 205, January 2010., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: elastix (RRID:SCR_009619) Copy
https://www.nitrc.org/projects/w2mhs/
An open source MATLAB toolbox designed for detecting and quantifying White Matter Hyperintensities(WMH) in Alzheimer?s and aging related neurological disorders.Our toolbox provides a self-sufficient set of tools for segmenting these WMHs reliably and further quantifying their burden for down-processing studies. WMHs arise as bright regions on T2-weighted FLAIR images. They reflect comorbid neural injury or cerebral vascular disease burden. Their precise detection is of interest in Alzheimer?s disease (AD) with regard to its prognosis.
Proper citation: Wisconsin White Matter Hyperintensities Segmentation Toolbox (RRID:SCR_009652) Copy
http://web.mit.edu/evelina9/www/funcloc.html
Spm-toolbox that performs region of interest (ROI)-level and voxel-level between-subjects analyses of functional MRI data, restricting the analyses to those areas identified using subject-specific functional localizers. Methods: The toolbox implements ROI-level and voxel-level analyses, and it implements an automatic cross-validation procedure when the localizers are not orthogonal to the effects-of-interest. ROI-level analyses allow manually defined parcels of interest, as well as automatically-defined ones (GcSS procedure, Fedorenko et al. 2010). General linear model second-level analyses are implemented, including ReML and OLS estimation of population level effects. Hypothesis testing includes standard univariate tests as well as multivariate tests for mixed within- and between-subject designs (T, F, and Wilks' lambda statistics) This toolbox requires Matlab and SPM5/SPM8.
Proper citation: SPM SS - fMRI functional localizers (RRID:SCR_009644) Copy
Neuromorphometrics provides brain labeling and measurement services. Given raw MRI brain scans, we make precise quantitative measurements of the volume, shape, and location of specific neuroanatomical structures. Web tool for brain measurement services. Used for modeling living human brain and make quantitative measurements of volume, shape, and location of specific neuroanatomical structures using given MRI brain scans. Automated analyses are manually guided, inspected and certified by a neuroanatomical expert. Resource of neuroanatomically labeled MRI brain scans database. Resource for neuroanatomical localization and identification: NeuAtlas.
Proper citation: Neuromorphometrics (RRID:SCR_005656) Copy
http://afni.nimh.nih.gov/afni/
Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.
Proper citation: Analysis of Functional NeuroImages (RRID:SCR_005927) Copy
http://www.nitrc.org/projects/nyu_trt/
EPI-images of 25 participants gathered during rest as well as anonymized anatomical images of the same participants. The resting-state fMRI images were collected on several occasions: # the first resting-state scan in a scan session # 5-11 months after the first resting-state scan # about 30 (< 45) minutes after 2. Each scan occasion is released as a new version release of the resource. ---Caution: Participants here are part of the NewYork_a contribution to the 1000 Functional Connectomes Project. DO NOT combine datasets.
Proper citation: NYU CSC TestRetest (RRID:SCR_005975) Copy
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