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SICLE / Small-deformation Inverse-Consistent Linear-Elastic image Registration
Allows users to estimate the forward and reverse transformation between two images. SICLE is an image registration algorithm that determines the forward and reverse transformation between them while minimizing the inverse consistency error. It acts like that to cut the correspondence between these transformations associated with large inverse consistency errors. The estimated transformations are regularized thanks to a thin-plate spline (TPS) model.
AAL / Automated Anatomical Labeling
Offers a reference frame for activation labeling. AAL is based on an anatomical parcellation of the Montreal Neurological Institute (MNI) single-subject brain. It makes the correspondence between the coordinates obtained from images normalized with the MNI average template as the target and the Talairach atlas brain. The tool uses the terminology for sulci to parcellate the inferior surface of the frontal lobe corresponding to the orbitofrontal cortex areas. The method aims to suppress the confusion existing in the literature regarding the relationship between a set of coordinates and its anatomical label.
Lead-DBS / Lead Deep Brain Stimulation
Aims to assists users in spotting and displaying electrodes in patients treated with deep brain stimulation (DBS). Lead-DBS is a program that gathers tools facilitating DBS electrode reconstructions and related processing. This toolkit works by using 3D visualization mode and electrode reconstruction algorithms. It includes the following features: (1) reconstruction of the electrode trajectories; (2) manual correction of the electrode localization; or (3) visualization of a 3D model showing DBS electrodes and their target areas.
ANIMAL / Automatic Nonlinear Image Matching and Anatomical Labeling
Provides method to evaluate design choices and to choose parameter values. ANIMAL method assists users to realize comparison between competing spatial normalization algorithms. This method is based on multi-scale, three-dimensional (3D) cross-correlation to record a given volumetric data set to an average Magnetic Resonance Imaging (MRI) brain. Direct comparison of two or more data sets brought into stereotaxic space is allowed by this tool.
Allows the user to quickly configure, test, and compare different registration methods for a specific application. elastix is a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. It has a command-line interface, which simplifies batch-processing of large numbers of data sets. Registration of large 3-D images can be done efficiently, thanks to the use of stochastic subsampling techniques.
Dipy / Diffusion Imaging in Python
Allows to study diffusion Magnetic Resonance Imaging (MRI) data. Dipy is a program allowing users to share their code and experiments. One of its objectives is to provide transparent implementations for all the different steps of the dMRI analysis with a uniform programming interface. It implements two interfaces for probabilistic Markov fiber tracking: (1) it allows the user to provide the distribution evaluated on a discrete set of possible tracking directions, and (2) it accommodates tracking methods where the fiber orientation distribution function (fODF) cannot be easily computed.
Allows the analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies. FreeSurfer proposes a suite of tools that provide extensive and automated analysis of key features in the human brain. This includes skull stripping, image registration, subcortical segmentation, cortical surface reconstruction, cortical segmentation, cortical thickness estimation, longitudinal processing, fMRI analysis, tractography, FreeView Visualization GUI, etc... FreeSurfer is freely available, runs on a wide variety of hardware and software platforms, and is open source.
BBR / Boundary-Based Registration
An alignment procedure by maximizing image contrast across tissue boundaries rather than matching intensities between two images or by matching surface shapes. . BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial brain images to whole brain images, a domain in which existing registration algorithms frequently fail. BBR is part of the FreeSurfer software package.
ANTs / Advanced Normalization Tools
Computes high-dimensional mappings to capture the statistics of brain structure and function. ANTs allows users to organize, visualize and statistically explore large biomedical image sets. It integrates imaging modalities and related information in space and time, and works across species or organ systems with minimal customization. ANTs depends on the Insight ToolKit (ITK), a widely used medical image processing library to which ANTs developers contribute. ANTs can be used paired with ANTsR, an emerging tool supporting standardized multimodality image analysis. ANTs is popularly considered a state-of-the-art medical image registration and segmentation toolkit.
Evaluation of similarity measures
Provides a protocol that enables a thorough, optimization-independent, and systematic statistical evaluation of important similarity measure properties. Evaluation of similarity measures includes Accuracy (ACC), Distinctiveness of the Optimum (DO), Capture Range (CR), Number of Local Minima (NOM), Risk of Non-convergence (RON). The evaluation consists of three steps: (i) sampling of the parametrical space, (ii) computation of similarity measure values and (iii) computation of similarity measure properties.
Mango / Multi-image Analysis GUI
Automates regional behavioral analysis of human brain images. Mango provides analysis tools and a user interface to navigate image volumes. The tool is ease to use, multi-platform Java application and extensive region of interest tools. It has the ability to add and update software as a plugin module and offers full access to a suite of image viewing and processing features. The software is able to rapidly determine regionally specific behaviors for researchers’ brain studies.
AMIDE / Amide's a Medical Imaging Data Examiner
Displays and analyzes multimodality volumetric medical images. AMIDE provides the research community with a relatively full-featured, freely available, and open source solution for single and multimodality volumetric medical image analysis. It provides a variety of additional features useful to the molecular imaging researcher, including fully three dimensional ROI drawing and analysis for static and dynamic images, two and three way linked viewing (dual cursor mode), rigid body registration using fiducial markers, filtering and cropping of data sets, movie generation, series viewing, and volume rendering.
Allows the generation of tissue class priors from infancy to old age. CerebroMatic is a toolbox that uses a more flexible statistical approach, namely multivariate adaptive regression splines. It was developed for use within the spm software environment. This method allows user to generate matched tissue probability maps for tissue segmentation and spatial normalization. It is based on statistically-generated regression parameters from a large sample of healthy infants, children, and adults.
ITK / Insight Segmentation and Registration Toolkit
Allows to perform registration and segmentation for image analysis. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. ITK uses a model of software development known as Extreme Programming. The sampled representation is an image acquired from such medical instrumentation as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or ultrasound scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.
NMT / National Institute of Mental Health Macaque Template
Provides a high-resolution in vivo magnetic resonance imaging template of the average macaque brain generated from 31 subjects, as well as a neuroimaging tool for improved data analysis and visualization. From the NMT volume, maps of tissue segmentation and cortical thickness were generated. Surface reconstructions and transformations to previously published digital brain atlases are also provided. An analysis pipeline using the NMT automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and morphometric feature estimation for anatomical scans of individual subjects. The NMT and associated tools thus provide a common platform for precise single-subject data analysis and for characterizations of neuroimaging results across subjects and studies.
Deep QC / Deep Quality Control
Furnishes a deep learning solution for magnetic resonance imaging (MRI) registration quality control (QC). Deep QC can be used for automatic testing of the robustness of image registration methods. It enables users to rerun the registration methods with different settings and parameters to ensure a high acceptance rate for their cohort of interest. However, it can be incorporated into the process of the registration to enforce the method to repeat registration with different parameter settings until an accepted registration is obtained.
TORTOISE / Tolerably Obsessive Registration and Tensor Optimization Indolent Software Ensemble
Processes the diffusion of magnetic resonance imaging (MRI) data. TORTOISE contains three main modules: DIFF_PREP-software for image resampling, motion, eddy current distortion, and EPI distortion correction using a structural image as target, and for re-orientation of data to a common space; DIFF_CALC-software for tensor fitting, error analysis, directionally encoded color map visualization and ROI analysis; DR-BUDDI-software for EPI distortion correction using pairs of diffusion data sets acquired with opposite phase encoding (blip-up blip-down acquisitions).
Connectome Mapping Toolkit
A set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison.
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