Artefact segmentation in digital pathology whole-slide images

Dataset with examples of Artefacts in Digital Pathology.

The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.

The dataset is split in different folders:

train

18 whole-slide images (extracted at 1.25x & 2.5x magnification)

All from the same Block (colorectal cancer tissue)

1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.

validation

3 whole-slide images (1.25x + 2.5x mag)

2 from the same Block as the training set (1 IHC, 1 H&E)

1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)

validation_tiles

patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.

7 patches from each slide.

test

1 whole-slide image (1.25x + 2.5x mag)

From another block: IHC staining (anti-NR2F2), mouth cancer

For the train, validation and test whole-slide images, each slide has:
- The RGB images @1.25x & 2.5x mag
- The corresponding background/tissue masks
- The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)

For the validation tiles, the following table gives the "patch-level" supervision:

tile#   Artefact(s)
00      None/Few
01      Tear&Fold
02      Ink
03      None/Few
04      None/Few
05      Tear&Fold
06      Tear&Fold + Blur
07      Knife damage
08      Knife damage
09      Ink
10      None/Few
11      Tear&Fold
12      Tear&Fold
13      None/Few
14      None/Few
15      Knife damage
16      Tear&Fold
17      None/Few
18      None/Few
19      Blur
20      Knife damage