Skip to content

AICAN-Research/estrogen-receptor-prediction

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 

Repository files navigation

Esterogen receptor status prediction

Pre- and postprocessing scripts for prediction of biomarker status from HE-stained TMA-cores using. This repository is used to preprocess TMA images to use in the CLAM framework. TMA cores extracted and saved on disk as one image per patient.

Datasets

To use the code, you need this:

  • TMA slides (.tif)
  • TMA maps or lists of which ids corresponds to which TMA core
  • csv-file with biomarker status for each patient

Create dataset of TMA cores

  1. Annotate TMA-slides, correct id for each TMA core, in QuPath:
    1. Use TMA dearrayer to create a grid
    2. Run tma-to-annotations.groovy for all TMA-slides and add correct ids in the script
  2. Export TMA cores as separate .tiff images using export_cores.groovy.
  3. Combine TMA cores from each patient into single images
    1. If you have patient level labels, not TMA core level labels, you need to combine the TMA core images for each patient into a single image. For example if for id X you have three TMA cores and thus three .tiff images (X_a.tiff, X_b.tiff and X_c.tiff) you have to combine these into one image, X.tiff by:
       python /path/to/repository/estrogen-receptor-prediction/preprocess/combine_tmas.py
      

Note: QuPath script may be run from command line, see QuPath documentation: Command line.

Biomarker Prediction

The created core triplets can then be used for example in the CLAM fork to predict biomarkers.

Postprocessing

Scripts for visualization or sorting of csv files and eventfiles from tensorboard can be found in the postprocess folder.

Acknowledgements

A big thank you to @petebankhead and the QuPath team for support and examples when implementing the groovy scripts.

Thank you to @Mahmoodlab for their CLAM pipeline:

@article{lu2021data,
  title={Data-efficient and weakly supervised computational pathology on whole-slide images},
  author={Lu, Ming Y and Williamson, Drew FK and Chen, Tiffany Y and Chen, Richard J and Barbieri, Matteo and Mahmood, Faisal},
  journal={Nature Biomedical Engineering},
  volume={5},
  number={6},
  pages={555--570},
  year={2021},
  publisher={Nature Publishing Group}
}

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published