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.
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.
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.
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.
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.
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