Debian Science Cognitive Neuroscience packages

AMIDE (от: исследователь данных медицинских изображений) AMIDE — инструмент для просмотра и анализа комплектов данных медицинских изображений. Он позволяет выполнять одновременную обработку сложных комплектов данных, импортируемых из множества форматов файлов, слияние изображений, вывод и анализ интересующей трёхмерной области, визуализация объёмов и взаимное выравнивание снимков твёрдых тел.

Amide поддерживает импорт большинства клинических DICOM файлов, благодаря библиотеке DCMTK.

Утилиты командной строки для создания, изменения, выгрузки и проверки файлов с DICOM-атрибутами. Поддерживается преобразование некоторых проприетарных форматов в DICOM. Возможна работа с более старым форматом данных ACR/NEMA и некоторыми проприетарными версиями этого формата, такими как SPI.

Программа dinifti конвертирует изображения MRI в формате DICOM в формат NIfTI. Под форматом NIfTI понимается новый стандарт формата изображений для медицинских изображений, и он может использоваться, к примеру, с FSL, AFNI, SPM, Caret или Freesurfer.

dinifti конвертирует отдельные файлы, но также поддерживает полностью автоматическое пакетное преобразование каталогов с файлами dicom. Получаемые в результате преобразования файлы NIfTI могут получать имя на основе серийной информации файлов DICOM.

SNAP предоставляет возможность полуавтоматической сегментации структур в медицинских изображениях (например, магнитно-резонансных изображениях мозга) при помощи метода активных контуров, а также ручного разграничения и навигации по изображению. Следует отметить следующие особенности:

• привязка курсора для плавной 3D-навигации;
• ручная сегментация одновременно в трёх ортогональных проекциях;
• поддержка различных форматов 3D-изображений, включая NIfTI;
• одновременный просмотр и сегментация нескольких изображений;
• ограниченная поддержка цветных изображений (например, карты тензора диффузии);
• инструмент обрезки 3D-проекции для ускорения последующей обработки результатов сегментации.

This project stands for Medical Image Conversion. Released under the (L)GPL, it comes with the full C-source code of the library, a flexible command line utility and a neat graphical front-end using the GTK+ toolkit. The currently supported formats are: Acr/Nema 2.0, Analyze (SPM), DICOM 3.0, InterFile 3.3 and PNG.

The program also allows one to read unsupported files without compression, to print pixel values or to extract/reorder specified images. It is possible to retrieve the raw binary/ascii image arrays or to write PNG for desktop applications.

This is the command line tool for batch processing.

This package contains tools to manipulate MINC files.

The Minc file format is a highly flexible medical image file format built on top of the NetCDF generalized data format. The format is simple, self-describing, extensible, portable and N-dimensional, with programming interfaces for both low-level data access and high-level volume manipulation. On top of the libraries is a suite of generic image-file manipulation tools. The format, libraries and tools are designed for use in a medical-imaging research environment: they are simple and powerful and make no attempt to provide a pretty interface to users.

MRIConvert — утилита преобразования медицинских изображений, которая переводит файлы DICOM в форматы NIfTI 1.1, Analyze 7.5, SPM99/Analyze, BrainVoyager и MetaImage.

Инструмент с графическим интерфейсом для визуализации и анализа (функциональной) магнитно-резонансной томографии. MRIcron может быть использован для создания 2D и 3D визуализаций статистических перекрывающихся карт анатомических изображений мозга. Кроме того, он помогает в прорисовке анатомических "областей интереса" (ROI), или отображении повреждений, а также базового анализа функциональных таймсерий (например, создание графиков изменения сигнала перистимулов).

В дополнение к 'mricron' пакет содержит утилиту 'dcm2nii', которая поддерживает преобразование DICOM и PAR/REC изображений в формат NIfTI, и утилиту 'npm' для непараметрического анализа данных.

Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images.

This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test).

Nifti2Dicom is a conversion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS.

This package includes the command line tools.

According to its authors, praat is "doing phonetics by computer". Through its graphical interface, several speech analysis functionalities are available: spectrograms, cochleograms, and pitch and formant extraction. Articulatory synthesis, as well as synthesis from pitch, formant, and intensity are also available. Other features are segmentation, labelling using the phonetic alphabet, and computation of statistics. Praat is configurable and extensible through its own scripting language and has provisions for communicating with other programs.

Psignifit allows fitting of psychometric functions to datasets while maintaining full control over a large number of parameters. Data can either be read from text files or passed through a pipe.

Psignifit performs the calculation of confidence intervals as well as goodness-of-fit tests.

This is the command line version.

Please note: This is the legacy version 2.x of psignifit.

NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI.

pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects.

pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files.

This package installs the module for Python 3.

pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features:

• resources browsing capabilities
• read and write access to resources
• complex searches
• disk-caching of requested files and resources

statsmodels Python3 module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for several distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem.

Nifti2Dicom is a conversion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS.

This package contains the Qt5 GUI.

Это инструмент для анализа данных, полученных в функциональных нейровизуализационных экспериментах (главным образом ФМРТ), и пространственной связи патологий. VoxBo поддерживает модифицированные обобщённые линейные модели (GLM) для автокоррелированных данных, помимо стандартных. Имеется возможность обмена данными с AFNI, FSL, SPM и другими инструментами.

This project stands for Medical Image Conversion. Released under the (L)GPL, it comes with the full C-source code of the library, a flexible command line utility and a neat graphical front-end using the GTK+ toolkit. The currently supported formats are: Acr/Nema 2.0, Analyze (SPM), DICOM 3.0, InterFile 3.3 and PNG.

The program also allows one to read unsupported files without compression, to print pixel values or to extract/reorder specified images. It is possible to retrieve the raw binary/ascii image arrays or to write PNG for desktop applications.

This is the program version for X based on GTK+. Processes only one file at a time.

The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics.

Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit.

BioXTAS RAW is a GUI based, Python program for reduction and analysis of small-angle X-ray solution scattering (SAXS) data. The package is designed for biological SAXS data.

BioXTAS RAW provides an alternative to closed source programs such as Primus and Scatter for primary data analysis. Because it can calibrate, mask, and integrate images it also provides an alternative to synchrotron beamline pipelines that scientists can install on their own computers and use both at home and at the beamline.

This metapackage will install Debian packages which might be useful for carrying out any experiment relating physical stimuli and their psychological effects.

The selection of packages is targeting software for stimuli delivery. For additional software related to the analysis of the acquired data refer to science-neuroscience-cognitive, med-imaging depending on the domain of application. Additionally look into science-bci since those often provide a complete loop frameworks including stimuli delivery.

This metapackage will install Debian Science packages related to typesetting. You might also be interested in the use::typesetting debtag.

The only purpose of this package is to enable upgrades to the new 'fsl-core' package which replaces 'fsl'. This package can safely be removed.

Users aiming to perform a complete FSL installation (including all data components) are advised to install the 'fsl-complete' package from NeuroDebian.

BioImage Suite has extensive capabilities for both neuro/cardiac and abdominal image analysis and state of the art visualization. Many packages are available that are highly extensible, and provide functionality for image visualization and registration, surface editing, cardiac 4D multi-slice editing, diffusion tensor image processing, mouse segmentation and registration, and much more. It can be integrated with other biomedical image processing software, such as FSL and SPM. This site provides information, downloads, documentation, and other resources for users of the software.

BioImage Suite was developed at Yale University and has been extensively used at different labs at Yale since 2004.

Stimulus counter-balance is important for many experimental designs. This command-line software creates De Bruijn cycles, which are pseudo-random sequences with arbitrary levels of counterbalance. "Path-guided" de Bruijn cycles may also be created. These sequences encode a hypothesized neural modulation at specified temporal frequencies, and have enhanced detection power for BOLD fMRI experiments.

FreeSurfer is a set of tools for analysis and visualization of structural and functional brain imaging data. It contains a fully automatic structural stream for processing cross sectional and longitudinal data.

FreeSurfer provides many anatomical analysis tools, including: representation of the cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from the rest of the brain, skull stripping, B1 bias field correction, nonlinear registration of the cortical surface of an individual with an sterotaxic atlas, labeling of regions of the cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures, etc.

This package depends upon the latest version of freesurfer.

OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography).

This package provides command line interface to openmeeg functionality.

Slicer is an application for computer scientists and clinical researchers. The platform provides functionality for segmentation, registration and three-dimensional visualization of multi-modal image data, as well as advanced image analysis algorithms for diffusion tensor imaging, functional magnetic resonance imaging and image-guided therapy. Standard image file formats are supported, and the application integrates interface capabilities to biomedical research software and image informatics frameworks.

3D Slicer main application.

The primary functionality of XNAT is to provide a place to store and control access to neuroimaging data. This includes sophisticated user control, search and retrieval, and archiving capabilities. As open-source software, XNAT also supports a wide variety of research-based processing pipelines, and is able to link up with supercomputer processing power to dramatically shorten image processing time.

EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data.

This package provides a version of the MNI Average Brain (an average of 305 T1-weighted MRI scans, linearly transformed to Talairach space) specially adapted for use with the MNI Linear Registration Package.

• average_305.mnc - a version of the average MRI that covers the whole brain (unlike the original Talairach atlas), sampled with 1mm cubic voxels
• average_305_mask.mnc - a mask of the brain in average_305.mnc
• average_305_headmask.mnc - another mask, required for nonlinear mode

Remark: Michael Hanke agreed to take over his stuff from mentors http://mentors.debian.net/cgi-bin/sponsor-pkglist?action=details;package=mni-autoreg and http://mentors.debian.net/cgi-bin/sponsor-pkglist?action=details;package=mni-autoreg-model to Debian Med svn and start group maintenance.

MNI Non-parametric Non-uniformity Normalization (N3). This package provides the 'nu_correct' tool for unsupervised correction of radio frequency (RF) field inhomogenities in MR volumes. Two packages are provided:

• mni-n3 - provides 'nu_correct'
• libebtks-dev - MNI support library with numerical types and algorithms

Remark: Michael Hanke agreed to take over his stuff from mentors http://mentors.debian.net/cgi-bin/sponsor-pkglist?action=details;package=mni-n3 to Debian Med svn and start group maintenance.

The software includes algorithms for simple and advanced analysis of MEG and EEG data, such as time-frequency analysis, source reconstruction using dipoles, distributed sources and beamformers and non-parametric statistical testing. It supports the data formats of all major MEG systems (CTF, Neuromag, BTi) and of the most popular EEG systems, and new formats can be added easily. FieldTrip contains high-level functions that you can use to construct your own analysis protocols in Matlab. Furthermore, it easily allows developers to incorporate low-level algorithms for new EEG/MEG analysis methods.

This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data.

PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets.

Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG.

BrainVISA is a software, which embodies an image processing factory. A simple control panel allows the user to trigger some sequences of treatments on series of images. These treatments are performed by calls to command lines provided by different laboratories. These command lines, hence, are the building blocks on which are built the assembly lines of the factory. BrainVISA is distributed with a toolbox of building blocks dedicated to the segmentation of T1-weighted MR images. The product of the main assembly line made up from this toolbox is the following: grey/white classification for Voxel Based Morphometry, Meshes of each hemisphere surface for visualization purpose, Spherical meshes of each hemisphere white matter surface, a graph of the cortical folds, a labeling of the cortical folds according to a nomenclature of the main sulci.

The Human Imaging Database (HID) is an extensible database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large.

This graphical environment allows for designing large-scale multi-level neuronal systems that can control real-world devices, such as cameras and mobile robots, in real-time. iqr is an extensible framework with the ability to handle new, user-provided, neuron and synapse types, as well as custom interfaces to other hardware systems. iqr employes an XML-based format for system descriptions.