Semi-automated

The MorphoLeaf application allows you to extract the contour of multiple leaf images and identify their biologically-relevant landmarks. These landmarks are then used to quantify morphological parameters of individual leaves and to reconstruct average leaf shapes. MorphoLeaf is developed by the Modeling and Digital Imaging and the Transcription Factors and Architecture teams of the Institut Jean-Pierre Bourgin, INRA Versailles, France, and the Biophyscis and Development group at RDP, Lyon.

arivis Vision4D is a modular software for working with multi-channel 2D, 3D and 4D images of almost unlimited size independent of available RAM. Many imaging systems, such as high speed confocal, Light Sheet/ SPIM and 2 Photon systems, can produce a huge amount of multi-channel data, which arivis Vision4D handles without constraints. Terabyte ready arivis Vision4D main functionality: Easy import of most image formats from microsopes as well as biological formats High performance interactive 3D / 4D rendering on standard PCs and laptops with 3D Graphics Support Intuitive tools for stitching and alignment to create large multi-dimensional image stacks Immediate 2D, 3D and 4D visualization, annotation and analysis regardless of image size Creation, import, and export of 4D Iso-surfaces Powerful Analysis Pipeline for 3D /4D image analysis (cell segmentation, tracking, annotation, quantitative measurement and statistics, etc) Semi-automatic/manual segmentation and tracking with interactive Track Editor Easy design and export of 3D / 4D High Resolution Movies Seamless integration of custom workflows via Matlab API and Python scripting Data sharing for collaboration A user friendly software, easy to learn and use for any life scientist

Free-D (http://free-d.versailles.inra.fr/) is a 3D reconstruction and modeling software. It is multiplatform, free (but not open source) tool for academic research and teaching.

Here is how to proceed, using Free-D:

1. Segmentation:

* load (a collection of) individual 3d stacks

* (optional for serial sections) perform a 2D registration to align image slices

* segment/reconstruct 3D contours using snakes

* segment 3D spots

2. Construct average cell:

* normalize the contours to compute a average cell, by registering/warping 3D contours/surfaces

3. Quantification:

* project each individual cell to the average one

* build density maps to analyze (cartography)

A few notes for current software version (till 10/2016):

* input file format: tiff (not able to import bioformats)

* currently results are saved in customized format, but there is an exportor to convert this format into fiji readable one

* import already generated contours is on the software's TODO list

This is an ImageJ plugin to analyze bacterial cells. It provides a user-friendly interface and a powerful suite of detection, analysis and data presentation tools. It works with individual phase or fluorescence images as well as stacks, hyperstacks, and folders of any of these types. Even large image sets are analyzed rapidly generating raw tabular data that can either be saved or copied as is, or have additional statistical analysis performed and graphically represented directly from within MicrobeJ, making it an all-in-one image analysis solution.

In this workflow, you can use MorphoLibJ to generate accurate morphometric measurements

• First the fibers are segmented by mathematical morphology:
  • for example by using MorphoLibJ:
    • Create a marker image by creating a rough mask with extended regional maxima (similar to Find Max), such that you have one max per fiber
    • Use the marker controlled watershed (in MorpholLibJ/ Segmentation/ marker controlled watershed) : indicate the original grayscale image as the input, Marker will be your maxima image, select None for mask
    • it will create a label mask of your fibers
•  In MorphoLibJ /analyze/ select Region Morphometry: this will compute different shape factors which are more robust than the ones implemented by default in ImageJ
• Export the result table created to a csv file
• Then for example in Matlab or R, you can apply a PCA analysis (Principal component analysis) followed by a k-means with the number of class (clusters) (different fibers type) you want to separate.
• You can then add this class as a new feature to your csv file.
• From this you can sort your labelled fibers into these clusters for a visual feedback or further spatial analysis