Bioimage informatics

The OpenCV CUDA module is a set of classes and functions to utilize CUDA computational capabilities. It is implemented using NVIDIA* CUDA* Runtime API and supports only NVIDIA GPUs. The OpenCV CUDA module includes utility functions, low-level vision primitives, and high-level algorithms. The utility functions and low-level primitives provide a powerful infrastructure for developing fast vision algorithms taking advantage of CUDA whereas the high-level functionality includes some state-of-the-art algorithms (such as stereo correspondence, face and people detectors, and others) ready to be used by the application developers.

The CUDA module is designed as a host-level API. This means that if you have pre-compiled OpenCV CUDA binaries, you are not required to have the CUDA Toolkit installed or write any extra code to make use of the CUDA.

The module provides biological visual systems models (human visual system and others). It also provides derivated objects that take advantage of those bio-inspired models.

Quote: "The GDSC ImageJ plugins are a collection of analysis programs for microscopy images including colocalisation analysis and peak finding (FindFoci)."

Many types of analysis besides simply finding foci detection (spot detection) is bundled in this plugin. One prominent function is "FindFoci Optimizer". This allows feeding images with spot annotation by the user (multi-point selection tool) and scans through various parameter combinations to find the best parameter set that gives the results similar to the annotation. This is almost like machine learning... but with well-established parameter types that allows you to fully understand what is going on.

This plugin filters a 3D image stack (or 2D image) to produce a score for how "tube-like" each point in the image is. This is useful as a preprocessing step for tracing neurons or blood vessels, for example. For 3D image stacks, the plugin uses the eigenvalues of the Hessian matrix to calculate this measure of "tubeness", using a metrics mentioned in Sato et al 1997 ¹: if the larger two eigenvalues (λ₂ and λ₃) are both negative then value is √(λ₂λ₃), otherwise the value is 0. For 2D images, if the large eigenvalue is negative, we return its absolute value and otherwise return 0.

This plugin is now bundled as part of Fiji.