GPU

BiaPy is an open source Python library for building bioimage analysis pipelines. 

BiaPy contains ready-to-use solutions for the tasks of semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification. 

TGMM is a cell tracking solution for large 3D volume (typically lightsheet).

It detects cell nuclei by fitting gaussians on their fluorescent intensity.

It can run on GPU using CUDA and is called via the command line.

CLIJ2 is a GPU-accelerated image processing library for ImageJ/Fiji, Icy, Matlab and Java. It comes with hundreds of operations for filtering, binarizing, labeling, measuring in images, projections, transformations and mathematical operations for images. While most of these are classical image processing operations, CLIJ2 also allows performing operations on matrices potentially representing neighborhood relationships between cells and pixels.

CLIJ2 was developed to process images from fluorescence microscopy data of developing cells, tissues, organoids and organisms.

Quantitative Criterion Acquisition Network (QCA Net) performs instance segmentation of 3D fluorescence microscopic images. QCA Net consists of Nuclear Segmentation Network (NSN) that learned nuclear segmentation task and Nuclear Detection Network (NDN) that learned nuclear identification task. QCA Net performs instance segmentation of the time-series 3D fluorescence microscopic images at each time point, and the quantitative criteria for mouse development are extracted from the acquired time-series segmentation image. The detailed information on this program is described in our manuscript posted on bioRxiv.