# Learn2Reg – Abdomen MR-CT (TCIA Subset)

## License
Because Learn2Reg sourced images from different datasets and here we only used the TCIA-relevant subset, the license is as follows:  
TCIA (TCGA-KIRC, TCGA-KIRP, TCGA-LIHC): [TCIA Data Usage Policy](https://www.cancerimagingarchive.net/data-usage-policies/) and [Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/).

## Citation
Paper BibTeX:

```bibtex
@article{hering2022learn2reg,
  title={Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning},
  author={Hering, Alessa and Hansen, Lasse and Mok, Tony CW and Chung, Albert CS and Siebert, Hanna and H{\"a}ger, Stephanie and Lange, Annkristin and Kuckertz, Sven and Heldmann, Stefan and Shao, Wei and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={42},
  number={3},
  pages={697--712},
  year={2022},
  publisher={IEEE}
}
```


## Dataset description
The Learn2Reg challenge provides datasets, annotations, and open-source evaluation code for developing and benchmarking medical image registration methods. The Abdomen MR-CT task includes CT scans with organ labels to support multi-modal abdominal image registration research.

**Challenge homepage**: https://learn2reg.grand-challenge.org/learn2reg-2025/

**Number of CT volumes**: 16

**Contrast**: -

**CT body coverage**: Abdomen

**Does the dataset include any ground truth annotations?**: Yes

**Original GT annotation targets**: Liver, spleen, right kidney, left kidney

**Number of annotated CT volumes**: 8

**Annotator**: Human

**Acquisition centers**: -

**Pathology/Disease**: -

**Original dataset download link**: (Task "Abdomen MR-CT") https://learn2reg.grand-challenge.org/Datasets/

**Original dataset format**: nifti

## Note
This subset contains 16 TCIA images from the Abdomen MR-CT task (sources: TCGA-KIRC, TCGA-KIRP, TCGA-LIHC), corresponding to imagesTr/ and imagesTs/ cases AbdomenMRCT_0001_0001 to AbdomenMRCT_0016_0001. Our internal IDs (learn2reg_img000x_tcia) do not match the original 1–16 numbering.