---
license: cc-by-nc-sa-4.0
pretty_name: RepConTUR AbdomenAtlas Annotations
task_categories:
  - image-segmentation
  - visual-question-answering
  - text-generation
tags:
  - medical
  - radiology
  - ct
  - 3d
  - tumor-segmentation
  - referring-segmentation
  - report-generation
size_categories:
  - 1K<n<10K
---

# RepConTUR AbdomenAtlas Annotations

Official annotations for "RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation".

This dataset extends [AbdomenAtlas 3.0 Mini](https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas3.0Mini) to
suborgan-level promptable tumor segmentation and report-derived visual question answering. It holds **annotations
only**: the CT volumes and the original masks must be downloaded from AbdomenAtlas 3.0 Mini.

- **Code:** <https://github.com/Oncosoft-Research/RepConTUR>
- **Model:** <https://huggingface.co/Oncosoft-Research/RepConTUR>
- **Paper:** <https://arxiv.org/abs/2610.40094>

## Contents

`annotations.json` covers the 3,798 AbdomenAtlas 3.0 Mini CT volumes that contain a liver, kidney or pancreas tumor.

| | Count |
|---|---|
| CT volumes | 3,798 (3,425 train, 373 test) |
| Tumor masks | 13,469 (4,843 whole-organ, 8,626 suborgan) |
| Referring phrases | 10 per mask |
| VQA pairs | 71,783 (48,451 yes/no, 13,433 short answer, 9,899 multiple choice) |

Each mask targets one location:

| Organ | Whole organ | Suborgan |
|---|---|---|
| Liver | all liver | hepatic segments 1–8 (Couinaud) |
| Kidney | all kidney | the left kidney, the right kidney |
| Pancreas | all pancreatic | the pancreatic head, body, tail |

The four tasks of the paper use these annotations as follows:

| Task | Input | Target |
|---|---|---|
| Referring segmentation (RES) | CT + a referring phrase | the tumor mask |
| Report-grounded segmentation (GSEG) | CT + narrative report + a referring phrase | the tumor mask |
| Report generation (RRG) | CT | the narrative report |
| Visual question answering (VQA) | CT + a question | the answer |

## Format

`annotations.json` follows a COCO-like layout:

```text
info          {description, min_lesion_size}
categories    [{id: 1, name: liver_lesion}, {id: 2, name: kidney_lesion}, {id: 3, name: pancreatic_lesion}]
images[]      id, bdmap_id, split (train | test), file_name (image_only/<bdmap_id>/ct.nii.gz),
              demographics {sex, age, contrast}, reports {structured_report, narrative_report},
              vqa[] {id, question, answer, type, options (multiple choice only)}
annotations[] id, image_id, category_id, location, size_voxels,
              source_mask (mask_only/<bdmap_id>/segmentations/<organ>_lesion.nii.gz),
              rle_per_slice[] (one COCO RLE per axial slice), sentences[] {sentence}
```

All paths are relative to the AbdomenAtlas 3.0 Mini root. `contrast` is null when the phase is not recorded.
Each mask is stored as one COCO RLE per axial slice. It decodes to a `(W, H, D)` binary volume on the voxel grid of
the volume's `ct.nii.gz`, in the same orientation as the NIfTI data array:

```python
import json
import nibabel as nib
from pycocotools import mask as mask_utils

dataset = json.load(open('annotations.json'))
images = {image['id']: image for image in dataset['images']}

annotation = dataset['annotations'][0]
image = images[annotation['image_id']]
tumor_mask = mask_utils.decode(annotation['rle_per_slice'])            # (W, H, D) uint8
ct = nib.load(f'AbdomenAtlas3.0Mini/{image["file_name"]}').get_fdata()  # same shape
print(annotation['location'], annotation['sentences'][0]['sentence'])
```

## Use with the RepConTUR code

Place the AbdomenAtlas 3.0 Mini volumes and masks under `data/abdomen_atlas/{image_only,mask_only}` in the code
repository, then:

```bash
hf download Oncosoft-Research/RepConTUR annotations.json --repo-type dataset --local-dir data/abdomen_atlas
```

The training configs read `data/abdomen_atlas/annotations.json`. The repository also contains the scripts that rebuild
this file from AbdomenAtlas 3.0 Mini (`suborgan_assignment`, then `vqa_generation`).

## How it was built

- **Suborgan masks.** Each whole-organ tumor mask is split into connected components, and each component is scored
  against the AbdomenAtlas suborgan masks (liver Couinaud segments, left or right kidney, pancreatic head, body or
  tail) by intersection over component (IoC) and surface distance. Each suborgan the structured report names takes as
  many components as the report counts there, best-ranked first. Every remaining component goes to its highest-IoC
  suborgan and to any other suborgan it overlaps by IoC ≥ 0.2, or to the nearest suborgan if it overlaps none. A
  component can therefore belong to two adjacent suborgans. The components in one suborgan are merged into one mask.
- **Referring phrases.** Ten phrasings per mask are drawn from templates for the organ and location.
- **VQA.** Question–answer pairs are generated from the structured and narrative reports by a language model and
  checked by a second language model.
- **Reports.** The structured and narrative reports are those of AbdomenAtlas 3.0. They are synthetic, produced by the
  RadGPT pipeline, not written by radiologists.

## Limitations

- The reports are synthetic. They often give the largest tumor and the tumor count rather than every tumor's location.
- The released file is regenerated with the current scripts and differs slightly from the data the released model was
  trained on: that run had 8,354 suborgan masks, and its RRG and VQA tasks also used training volumes without a tumor.
- Research use only. Not for clinical decisions.

## License

The annotations adapt AbdomenAtlas 3.0 Mini (reports and tumor masks) and are released under the same
**CC BY-NC-SA 4.0** license: attribution, non-commercial use only, and adaptations under the same license. Cite
AbdomenAtlas 3.0 and RadGPT when you use this dataset, as well as:

```bibtex
@article{lee2026repcontur,
  title  = {RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation},
  author = {Lee, Seunghwan and Park, Sangjoon and Lee, Jongmin and Kim, Yoon Jo and Cho, Wonyoung and Kim, Jin Sung and Chae, Han Joo},
  journal = {arXiv preprint arXiv:2610.40094},
  year   = {2026}
}
```