Designed to extend any base neuron-tracing algorithm to be able to trace virtually unlimited data volumes. UltraTracer was applied to neuron-tracing algorithms with different design principles and tested on challenging human and mouse neuron datasets that have hundreds of billions of voxels. Results indicate that UltraTracer is scalable, accurate, and about 3 to 6 times more efficient compared to other state-of-the-art approaches.
Allows 3D image data annotation in various 3D image analysis settings in connectomics and other fields. webKnossos is an in-browser annotation tool. The software accelerates human 3D data interaction for electron microscopic (EM)-based connectomics in browser by about 4- to 13-fold, which likely saturates human interaction speed with 3D EM data of nervous tissue using flight mode.
Allows users to perform image analysis for high-resolution connectomics. SegEM is a program dedicated to reconstruct neuronal circuits. It supplies a classifier selection procedure permitting researchers to study different types of nerve tissue. This tool can solve the exchange between semi-automated reconstruction performance and synapse detection in high-resolution connectomics.
An interactive 3D axon tracking and labeling tool to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. AxonTracker-3D has been developed to facilitate the connectome function analysis in large-scale quantitative neurobiology studies. It can display the three orthogonal views of the current location of the centerline along with a visualization of the tracking results. The workflow consists of three steps: (i) re-slice the axon tubes along its orientation; (ii) extract 2D and 3D features from the slices and spheres rounding the center points; (iii) select samples to train AdaBoost classifier. Questions such as whether the spatial distribution of the axons are random in nature or follow a certain pattern can be answered with this tool.
Represents a deep learning method for segmenting 3D anisotropic brain electron microscopy images. DeepEM3D can efficiently build feature representation and incorporate sufficient multi-scale contextual information. DeepEM3D is able to produce highly accurate 3D neurite boundary probability maps, thereby requiring only a simple watershed method to do segmentation. This tool uses the power of inception and residual structures in the bottom-up path to integrate image information, and combines skip connection techniques with pyramid multi-scale contexture aggregation in the top-down path.
Allows users to perform a semi-automated 3D reconstruction of neurons. RhoANA is a six-stages analysis that starts from serial section electron microscopy images and which aims to be parallelizable on both computer clusters and GPUs. The application first: (i) ranks membranes; (ii) performs segmentation (iii) initiates block dicing; (iv) runs window fusion; (v) matches pairwise and lastly; (vi) remaps both local and global to create a final remapped block.
An open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction.
Permits to reconstruct complete connectome. NeuroProof is based on a threshold k determined heuristically. It is amenable to large-scale, crowd-sourcing efforts. The tool was used in the domain of electron microscopic (EM) reconstruction and can be applied to other domains. It provides routines for efficient agglomeration for an initial volume that is over-segmented.
It is an organized collection of software modules for image data handling, pre-processing, segmentation, inspection and editing, post-processing, and secondary analysis. These modules can be scripted to accomplish a variety of automated image analysis tasks.
A multi-user web-based collaborative management system for images and volumes which allows users to view multi-terabyte datasets, annotate images with their own annotation schema, and summarize the results. Viking has several key features. (1) It works over the internet using HTTP and supports many concurrent users limited only by hardware. (2) It supports a multi-user, collaborative annotation strategy. (3) It cleanly demarcates viewing and analysis from data collection and hosting. (4) It is capable of applying transformations in real-time. (5) It has an easily extensible user interface, allowing addition of specialized modules without rewriting the viewer.
Serves for montaging, alignment, analysis and visualization of serial sections. Reconstruct allows users to organize, transform and display different types of data. It is able to analyze series with large number of sections and images over a large range of magnifications. Moreover, this tool includes functionalities that simplify cropping, scaling and comparison of images.
Traces and analyzes neurites in fluorescence microscopy images. NeuronJ consists in a semi-automatic neurite tracing technique that employs a global optimization algorithm and second-order image feature analysis, making it robust against noise, varying or discontinuous background intensities, and varying or locally diminishing neurite contrast. It can thus be applied to a wide range of images without changing its parameters.
Assists users to detect multi-color fluorescently labeled axons in dense electron microscopy (EM) data. FluoEM consists of a set of experimental and computational tools for the virtual labeling of multiple axonal projection sources in connectomic 3D EM data of mammalian nervous tissue. Moreover, this method can be used for the identification of as many axonal projection sources in a single connectomic experiment as can be encoded at the lightmicroscopic level.
Gathers multiple training tasks for general neural networks and non-image data. ELEKTRONN is a standalone application intending to furnish features for the processing of 3D/2D circumvolution neural networks (CNNs), several optimization routines as well as a training pipeline able to handle both 2D and 3D images. This program can be used for a wide range of applications in image segmentation, localization or income prediction.
Combines state-of-the-art automated neuron tracing and machine learning-enabled neuron classification tools. Aivia provides methods for analyzing time-lapse images. It covers a wide range of applications such as cell/nuclei counting, cell/nuclei tracking, 3D neuron detection and analysis, machine learning cell classification, particle tracking, wound healing and calcium oscillation tracking. Aivia also comes with editing tools to help get even better results.
Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.
Reconstructs neuronal population from image stacks. NeuroGPS-Tree is built on NeuroGPS software. In NeuroGPS-Tree reconstruction, individual neuronal trees can be identified and quantified. NeuroGPS-Tree reconstructs neuronal populations by partially mimicking the strategy used by experienced annotators and progressively approaches an accurate reconstruction by repetitively using statistical information about neuronal morphology at multiple scales. These features make NeuroGPS-Tree an effective tool for analyzing data sets in which the neurite density is too complex for previously established methods. NeuroGPS-Tree is also suitable for the analysis of large-scale data sets, and it may be useful for mapping neuronal circuits.
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