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Preparation and processing of ome.tif images with Ilastik

mcmicro module for training and processing large images with Ilastik

CommandIlastikPrepOME.py

Script for preparing ome.tif images to be accessed by Ilastik. Exports hdf5 formats.

Headless Ilastik execution once the classifier is ready

mkdir prob_maps/ilastik
python CommandIlastikPrepOME.py --input exemplar-001.ome.tif --output prob_maps/ilastik/ --num_channels 12

To apply an existing classifier to an hdf5 file created in the previous step:

/path/to/ilastik/run_ilastik.sh --headless --project=classifiers/exemplar_001.ilp prob_maps/ilastik/exemplar-001.hdf5

For training follow these steps

Parameter Default Description
--input None Path to images (Ex: ./my_image.ome.tif ./my_image2.ome.tif)
--output None Path to output directory. Either single directory or number of directories=to number of images (Ex: ./my_outdir)
--nonzero_fraction <value> None Indicates fraction of pixels per crop above global threshold to ensure tissue and not only background is selected
--nuclei_index <index> 1 (1-based) Index of nuclei channel to use for nonzero_fraction argument
--crop Omitted If specified, crop regions for ilastik training
--num_channels <value> None Number of channels to export per image (Ex: 40 corresponds to a 40 channel ome.tif image)
--channelIDs <indices> None Integer (1-based) indices specifying which channels to export (Ex: 1 2 4)
--ring_mask Omitted Specify if you have a ring mask in the same directory to use for reducing size of hdf5 image
--crop_amount <integer> None Number of crops you would like to extract

For example: python CommandIlastikPrepOME.py --input /Users/joshuahess/Desktop/VV_40c.ome.tif /Users/joshuahess/Desktop/VV_40c_test.ome.tif --output /Users/joshuahess/Desktop/TestingIlastik --nonzero_fraction 0.5 --nuclei_index 1 --crop --crop_size 400 400 --num_channels 3 --channelIDs 1 2 3 --ring_mask --crop_amount 2