Debian Science Cognitive Neuroscience packages

AMIDE: (Amide's a Medical Imaging Data Examiner) AMIDE は医用画像データセットの表示および分析用ツールです。各種ファイル フォーマットからインポートされた複数のデータセットの同時処理、画像統合、 関心のある三次元領域の描画や分析、ボリュームレンダリング、そして剛体ア ラインメントの機能が含まれます。

Amide は (DCMTK ライブラリを用いて) ほとんどの臨床 DICOM ファイルをイン ポートします。

Command line utilities for creating, modifying, dumping and validating files of DICOM attributes. Support conversion of some proprietary medical image formats to DICOM. Can handle older ACR/NEMA format data, and some proprietary versions of that such as SPI.

dinifti プログラムは DICOM フォーマットで保存された MRI 画像を NIfTI フォー マットに変換します。NIfTI フォーマットは医用画像の新しい画像フォーマットと みなされており、例えば FSL、AFNI、SPM、Caret 又は Freesurfer と共用できます。

dinifti はファイルを一つずつしか変換できませんが、dicomdirs 全体に対する完 全に自動なバッチ変換をサポートします。さらに、変換された NIfTI ファイルは DICOM ファイルから取得した画像シリーズ情報を使って適切に命名されます。

SNAP provides semi-automatic segmentation of structures in medical images (e.g. magnetic resonance images of the brain) using active contour methods, as well as manual delineation and image navigation. Noteworthy features are:

• Linked cursor for seamless 3D navigation
• Manual segmentation in three orthogonal planes at once
• Support for many different 3D image formats, including NIfTI
• Support for concurrent, linked viewing and segmentation of multiple images
• Limited support for color images (e.g., diffusion tensor maps)
• 3D cut-plane tool for fast post-processing of segmentation results

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.

本パッケージには、MINC ファイル操作用ツールが含まれます。

Minc ファイルフォーマットは、柔軟性が高い医用画像ファイルフォーマット であり、NetCDF 汎用データフォーマットを基盤にしています。 このフォーマットは、シンプルで、自己完結しており、拡張性が高く、 移植性が高く、多次元をサポートしており、プログラミングインターフェイスは 低レベルのデータアクセスと高レベルのボリューム操作の両方をサポートしています。 そしてこのライブラリを使った汎用の画像ファイル操作ツール集が用意されています。 フォーマット、ライブラリとツールは、医用画像研究環境での利用を想定して設計 されています。それゆえ、これらはシンプルかつパワフルであり、ユーザにきれい な外見を提供しようとしてはいません。

このパッケージは GUI ベースの (機能的) 核磁気共鳴画像法のための可視化および 解析ツールです。 MRIcron は脳解剖画像上での 2D および 3D の統計的オーバーレ イマップ描画の作成に使用できます。さらに、(刺激前後の信号の変化のプロットを 作成するなどの) 基本的な機能的時系列の分析だけでなく、解剖学的関心領域や病 変マッピングの描画を手助けします。

このパッケージは mricron に加え、DICOM および PAR/REC フォーマットの画像を NIfTI フォーマットに変換する dcm2nii とノンパラメトリック法によるデータ解析 を行う npm を含んでいます。

Niftilib は、NIfTI-1 データフォーマットのファイルを読み書きするための I/O ライブラリです。NIfTI-1 とは、医学画像データ、すなわち、磁気共鳴画像 (MRI) や、functional MRI (fMRI) 脳画像のためのバイナリファイルフォーマットです。

本パッケージは、ライブラリに同梱されているツール群 (nifti_tool, nifti_stats および nifti1_test) を提供します。

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.

作者によると、praat は "doing phonetics by computer" の略称だそうです。 グラフィカルなインターフェイスを通じて、スペクトログラム、cochleogram、 そしてピッチやフォルマントの抽出といった複数の音声分析機能が利用できます。 調音分析に加え、ピッチやフォルマント、そして強度からの分析が利用できます。 セグメント分割、音声のアルファベットを使ったラベル付け、そして統計計算 といった機能も利用できます。Praat はスクリプト言語を通じた設定や機能が豊富 でい、他のプログラムとの通信の用意ができています。

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.

This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others.

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.