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.
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.
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.
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.
Aims to reconstruct neuronal feature skeletons from threedimensional single- or multi-photon image stacks. SpineLab is an application that permits users to rebuild the dendritic tree of identified green fluorescent protein (GFP)-expressing neurons. It is also able to identify individual dendritic spines and enables investigators to adjust all necessary parameters of image preprocessing and automated detection in real-time.
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.
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.