Commit 5b649b3f authored by Saad Jbabdi's avatar Saad Jbabdi
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Use markdown for README

parent 9423a97f
Cell Counting Project
-- Install Instructions
git clone https://git.fmrib.ox.ac.uk/saad/CellCounting.git
cd CellCounting
pip install .
-- Howto?
Most of the tools have a wrapper script that can be called from the command line. Below are examples of how to use these command line tools:
- Building a cell database
This is done in three steps.
** select_zones.py :
First, a high resolution whole brain slide is loaded to a basic visualer. The visualiser allows the user to zoom in and out and to change the contrast. Crucially, it allows the user to select sub-regions of the image for subsequent analyses. These subregions will typically contain some cells, but also the user could select regions with background and/or artefact that the DL model needs to learn to distinguish from cells.
** split_zones.py :
Second, each of the selected zones get subdivided into smaller square images that will be used for cell clicking. These images will typically be 256x256 but that is up to the user.
** click_cells.py :
Third, the lengthy process of cell clicking begins, where the user loads a folder containing a bunch of these small square images and click whenever there are cells.
** create_db.py :
Finally (yeah there are four steps) one can build a cell database containing smaller (64x64) images alongside information on the number of cells contained in each subimage.
- building a cell counting model
** train_model.py
A script is available for training a DL model on pre built databases.
- running the forward model
** forward_density.py
This script runs a pretrained forward DL model on an image and outputs densities.
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