Digitally scanned Lightsheet Microscopy
Selective Plane Illumination Microscopy
Light-sheet fluorescence microscopy
Lattice Light-sheet Microscopy
Dual-View inverted SPIM
Spherical aberrations assisted Extended Depth-of-field Lightsheet Microscopy
Bessel Beam Lightsheet Microscopy
single objective Selective Plane Illumination Microscopy
Hardware implementations: multidirectional SPIM
Clarity Optimized Lightsheet Microscopy
Multiview Selective Plane Illumination Microscopy
inverted SPIM

StarDist - ImageJ


This is the ImageJ/Fiji plugin for StarDist, a cell/nuclei detection method for microscopy images with star-convex shape priors ( typically for Dapi like staining of nuclei). The plugin can be used to apply already trained models to new images.


3D cell tracking using Gaussian Mixture Model (TGMM)


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.

has function
need a thumbnail



Web based viewer developped for google for very big data: 

Neuroglancer is a WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons). The segmentation has to be done before loading the dataset, it is not done Inside the viewer.

This is not an official Google product.

It has among other the nice feature of beeing able to generate url for sharing a specific view.

Note that the only supported browser for now are 

  • Chrome >= 51
  • Firefox >= 46



Tumor Blood Vessels: 3D Tubular Network Analysis

Bioimage Analyst

In this session, we will implement a simple ImageJ macro to segment and analyze the blood vessel network of a subcutaneous tumor. The analysis is fully performed in 3D, and possible strategies to extract statistics of the network geometry and interactively visualize the results are also discussed and implemented.

Automated workflow for parallel Multiview Reconstruction


Automated workflow for performing multiview reconstruction of large multiview, multichannel, multiillumination time-lapse SPIM data on a high performance computing (HPC) cluster or on a single workstation. 



Spimagine is a python package to interactively visualize and process time lapsed volumetric data as generated with modern light sheet microscopes (hence the Spim part). The package provides a generic 3D+t data viewer and makes use of GPU acceleration via OpenCL. If provides further an image processor interface for the GPU accelerated denoising and deconvolution methods of gputools.

It is only for display (no analysis). The only drawback: it does not handle multichannel time lapse 3D data (only one channel at a time).

has function

Microscope autopilot


AutoPilot is the open source project that hosts the general algorithm for fast and robust assessment of local image quality, an automated computational method for image-based mapping of the three-dimensional light-sheet geometry inside a fluorescently labeled biological specimen, and a general algorithm for data-driven optimization of the system state of light-sheet microscopes capable of multi-color imaging with multiple illumination and detection arms.

has function

Quality metric of 3D SPIM stacks


An ImageJ/Fiji macro which measures quality through two stacks of images assumed to be acquired from two opposite angle of views using gray-level standard deviation.

CSBDeep, a toolbox for Content-aware Image Restoration (CARE) in Knime


Deep learning based restoration, with guidelines for training. See also the Fiji plugin.

CSBDeep, a toolbox for Content-aware Image Restoration (CARE) in Fiji


Deep learning for fluorescence image restoration (denoising, deconvolution). Requires training on your data set but the procedure is described.