labsyspharm/unetcoreograph
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Great....yet another TMA dearray program. What does this one do?
Coreograph uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types.
Training sets were acquired at 0.2micron/pixel resolution and downsampled 1/32 times to speed up performance. Once the center of each core has been identifed, active contours is used to generate a tissue mask of each core that can aid downstream single cell segmentation. A GPU is not required but will reduce computation time.
Coreograph exports these files:*
Instructions for use:*
python UNetCoreograph.py
--imagePath
: the path to the image file. Should be tif or ome.tif--outputPath
: the path to save the above-mentioned files--downsampleFactor
: how many times to downsample the raw image file. Default is 5 times to match the training data.--channel
: which is the channel to feed into UNet and generate probabiltiy maps from. This is usually a DAPI channel--buffer
: the extra space around a core before cropping it. A value of 2 means there is twice the width of the core added as buffer around it. 2 is default--outputChan
: a range of channels to be exported. -1 is default and will export all channels (takes awhile). Select a single channel or a continuous range. --outputChan 0 10 will export channel 0 up to (and including) channel 10docker pull labsyspharm/unetcoreograph