pretty_name: Pan-Multiplex (Pan-M) dataset
tags:
- multiplexed imaging
- digital pathology
license: cc-by-sa-4.0
task_categories:
- image-segmentation
Pan-Multiplex (Pan-M) dataset
This dataset was constructed to train the Nimbus model for the publication "Automated classification of cellular expression in multiplexed imaging data with Nimbus".
The dataset contains multiplexed images from different modalities, tissues and protein marker panels. It was constructed by a semi-automatic pipeline, where the cell types assigned by the authors of the original studies that published the data, where mapped back to their expected marker activity. In addition, for 3 FoVs of each dataset, 4 expert annotators proofread ~1.1M annotations which served as the gold-standard for assesing the algorithm.
More details to the construction of the dataset can be found in the paper. The dataset consists of five subsets named codex_colon,mibi_breast,mibi_decidua,vectra_colon,vectra_pancreas, each in an individual folder.
After unzipping, the data should be stored in the following folder structure to use the code provided for training and inference. To construct the binary segmentation maps used for training, you can use the code in segmentation_data_prep.py and simple_data_prep.py in the training repository.
.
├── data
│ ├── codex_colon
│ ├── vectra_colon
│ ├── vectra_pancreas
│ ├── mibi_decidua
│ ├── mibi_breast
│ ├── experimental_results.csv
│ ├── gt_pred_ie_consolidated.csv
When using the dataset please cite
@article{rum2024nimbus,
title={Automated classification of cellular expression in multiplexed imaging data with Nimbus},
author={Rumberger, J. Lorenz and Greenwald, Noah F. and Ranek, Jolene S. and Boonrat, Potchara and Walker, Cameron and Franzen, Jannik and Varra, Sricharan Reddy and Kong, Alex and Sowers, Cameron and Liu, Candace C. and Averbukh, Inna and Piyadasa, Hadeesha and Vanguri, Rami and Nederlof, Iris and Wang, Xuefei Julie and Van Valen, David and Kok, Marleen and Hollman, Travis J. and Kainmueller, Dagmar and Angelo, Michael},
journal={bioRxiv},
pages={2024--05},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}