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CSIF computing's bioimage analysis projects

One of the roles of the CSIF is to provide expertise in assembling individual methods into complete bioimage analysis/processing pipelines for specific projects using Python and/or ImageJ.

Our primary purpose is to help you extract valuable information from raw image data.

If you would like to request help from CSIF for image analysis, please submit our ONLINE FORM.

For any question, please contact Cedric Espenel (espenel@stanford.edu).

PORTFOLIO

Nucleus regognition and relative position within the gonad using machine learning. Number of FOCI / Nucleus

This code implements the use of Machine Learning to find the position of each Nucleus in the image. It will give the relative position of the nucleus within the full gonad and count the number of FOCI per nucleus.

Project supporting

Link

  • Chloé Girard
  • Anne Villeneuve's lab

Nucleus segmentation and YAP intensity analysis

This code implements a quantitative image analysis pipeline for the segmentation of nucleus and cytoplasm of hESCs in various conditions. These reconstructed cell volumes can be used to measure fluorescence intensity in other channels in the confocal images.

Project supporting

Link

  • Eva Huang
  • Alex Dunn's lab

Mapping the mesh of the enteric nervous system

The script extract the number of neurons per ganglion, define what is a ganglion, find their shape, size and distribution..

Project supporting

Link

  • Julieta Gomez-Frittelli and Subhamoy Das
  • Julia Kaltschmidt's lab

Neuronal Activity

The script correct for any drift during imaging, extract the fluorescence traces and measure and plot the synchronicity between the cells.

Project supporting

Link

  • Humsa Vankatesh

  • Michelle Monje's lab

 

Assessing Sarcomeres length over time.

This code implements the use of Machine Learning to find the position of each Nucleus in the image. It will give the relative position of the nucleus within the full gonad and count the number of FOCI per nucleus.

Project supporting

Link

  • HaoDi Wu
  • Joseph Wu's lab

Tracking of cell division and infection by Toxoplasma Gondii

This pipeline allows to distinguish uninfected host cells from host cells that were infected during the course of the experiment, track the host cells over time, and then monitor each cell for division.

Project supporting

Link

  • Suchita Rastogi
  • John Boothroyd’s lab

Tissue segmentation -

The ImageJ script select the different section on slides and measure the area intensity of the foreground.

Project supporting

Link

  • Humsa Vankatesh
  • Michelle Monje's lab

Microarray analysis

The script indentifies every well in the microarray and quantify the fluorescence intensity for every channel. It also allows to dynamicaly interact with the result of the quantification.

Project supporting

Link

  • Vincent Dufour-Decieux
  • Shan X. Wang's lab