This repo contains the intestinal gland segmentation dataset from pT1 cancer patients. It includes:
-
Dataset: From each image there are 26 images of cropped glands (13 normal, 13 dysplastic).
- image_labels.csv: Classification label for each graph and image (normal or dysplastic)
- There is a folder for each image. In this folder there is a folder for each crop containing:
- Cropped out gland image (*-image.jpg)
- Annotation mask (*-gt.png)
- Excel file with the features (*-features.xlsx)
-
Text files:
- dataset_split.csv: reference, validation and test set split for all 4 cross-validations
- feature_overview.csv: list of all possible node features (with enumeration, mean and std)
- ged-costs.csv: parameters for the different experiments
-
Graphs:
- Base dataset: cell segmentation with features (just nodes)
- Paper graphs:
- Baseline
- Optimized graph
This dataset has been for published at the COMPAY19 Workshop (link to the paper). The parameters for the GED calculated in this paper can be found here.
The current state-of-the-art performance is 83.3±1.7 on the 4-fold CV. If you outperform this, let me know and I will add you to the list.
| Published by | Paper | Used method | Performance |
|---|---|---|---|
| Studer et al. | (COMPAY Workshop @ MICCAI 2019). | Improved bipartite graph-matching | 83.3±1.7 |
If you use this dataset in your publication cite this paper:
@inproceedings{studer2019graph, title={Graph-based Classification of Intestinal Glands in Colorectal Cancer Tissue Images}, author={Studer, Linda and Toneyan, Shushan and Zlobec, Inti and Dawson, Heather and Fischer, Andreas}, year={2019} booktitle={COMPAY Workshop, International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2019}, }
This work is part of a larger project. Find out more here.