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.
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 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.
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