PanopTILs
An integrated region and cell-level annotation dataset for panoptic segmentation of the breast tumor microenvironment
An integrated region and cell-level annotation dataset for panoptic segmentation of the breast tumor microenvironment
0: Exclude
1: Cancerous epithelium
2: Stroma
3: TILs
4: Normal epithelium
5: Junk/Debris
6: Blood
7: Other
8: Whitespace/Empty
Epithelium = 1 + 4
0: Exclude
1: Cancer nucleus
2: Stromal nucleus
3: Large stromal nucleus
4: Lymphocyte nucleus
5: Plasma cell / large TIL nucleus
6: Normal epithelial nucleus
7: Other nucleus
8: Unknown/Ambiguous nucleus
9: Background (non-nuclear material)
Epithelial = 1 + 6
Stromal = 2 + 3
TILs = 4 + 5
Liu S, Amgad M, Rathore MA, Salgado R, Cooper LA. A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs dataset. medRxiv 2022.01.08.22268814.
Amgad M, Elfandy H, Hussein H, Atteya LA, Elsebaie MA, Abo Elnasr LS, Sakr RA, Salem HS, Ismail AF, Saad AM, Ahmed J. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019 Sep 15;35(18):3461-7.
Amgad M, Atteya LA, Hussein H, Mohammed KH, Hafiz E, Elsebaie MA, Alhusseiny AM, AlMoslemany MA, Elmatboly AM, Pappalardo PA, Sakr RA. NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer. GigaScience. 2022 May 17;11.