| --- |
| 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} |
| } |
| ``` |
|
|
