Bioimage analysis tools
CeMic supports on bioimage analysis including image postprocessing, cell segmentations in 3D, 2.5D, 3D nuclei segmentation, 2.5D, 3D cell lineage tracking, different visualization etc
MorphoGraphX (MGX) is an open source software for image processing, visualization, segmentation in 2D, 3D and 2.5D.
MGX requires a GPU linux workstation with atleast 64GB RAM for processing images minimally.
MGX windows version is less tested and is used for visualization purpose only, runs on CPU.
https://morphographx.org/
AI-powered bioimage analysis. Bioimage zoo contains open source ML models from the scientific community. It list various published and unpublised models that can be used for image predictions using PlantSeg or other softwares. One could run models from bioimageZoo directly on PlantSeg Napari plugin.
https://bioimage.io/#/models
MorphoDynamX (MDX) is an extension of MGX that allows to load and visualize more than 4 images as was the limiting factor of MGX. MDX handles meshes differently. MDX is very useful to capture screenshots of a 3D timelapse series.
PlantSeg is an open source machine learning based image prediction and segmentation tool. PlantSeg offers a collection of ML models for confocal, lightsheet and generic purpose image predictions for cell outlines and nuclei.
https://github.com/kreshuklab/plant-seg
Cellpose is an open source machine learning based image prediction and segmentation tool. Cellpose often works for plant cell and or nuclei data. It allows to retrain the existing models.
https://github.com/MouseLand/cellpose
Imaris is a commercial software for image processing and rendering.
https://imaris.oxinst.com/
Arivis is a commercial software for image processing and rendering. It allows to process bioimages using machine learned models from other sources.
Huygens allows to perform image deconvolutions
https://svi.nl/Huygens-Deconvolution
Deep-learning instance segmentation tool for nuclei segmentation. Can be used standalone or via Napari/Fiji.
Machine-learning–based segmentation (using pixel and object classification). GUI-based.
https://www.ilastik.org/
2D/3D analysis plugins (e.g., 3D Viewer, MorphoLibJ, TrackMate). Modular and scriptable, less smooth GUI.
Modern Python-based visualization for 2D–5DPlugin-rich ecosystem (Cellpose, StarDist, PlantSeg, etc.)