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.
Inputs A tif or ome.tiff image multiple tissues, such as a tissue microarray.
Outputs Coreograph exports these files: 1. individual cores as tiff stacks with user-selectable channel ranges 2. binary tissue masks (saved in the 'mask' subfolder) 3. a TMA map showing the labels and outlines of each core for quality control purposes