Description

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.

Input

name:type
description
pattern

meta:map

Groovy Map containing sample information

image:file

OME-TIFF or TIFF file for core detection and extraction.

*.{ome.tif,tif,tiff}

Output

name:type
description
pattern

cores

meta:map

Groovy Map containing sample information e.g. [ id:‘test’ ]

*[0-9]*.tif:file

Complete/Incomplete tissue cores

*.{tif}

masks

meta:map

Groovy Map containing sample information e.g. [ id:‘test’ ]

masks/*.tif:file

Binary masks for the Complete/Incomplete tissue cores

./masks/*.{tif}

tma_map

meta:map

Groovy Map containing sample information e.g. [ id:‘test’ ]

TMA_MAP.tif:file

A TMA map showing labels and outlines

TMA_MAP.tif

centroids

meta:map

Groovy Map containing sample information e.g. [ id:‘test’ ]

centroidsY-X.txt:file

A text file listing centroids of each core in format Y, X

centroidsY-X.txt

versions

versions.yml:file

File containing software versions

versions.yml

Tools

coreograph

A TMA dearray program that uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray.

mcmicro.org/parameters/core.html#coreograph mcmicro.org/troubleshooting/tuning/coreograph https://github.com/HMS-IDAC/UNetCoreograph 10.1038/s41592-021-01308-y
mcmicro
Github user arozhada
Github user MargotCh
Ask a question on Slack Open an issue on GitHub