---
license: openmdw-1.1
task_categories:
- robotics
- image-to-video
tags:
- lerobot
- open-h
- synthetic
- bronchoscopy
- maisi
- nv-generate
- rflow-ct
- synairg
- controlnet
- pbr
- tum
private_synthetic_medical_data: true
pretty_name: 1000 Lungs
---

<h1 align="center"><strong>1000lungs</strong></h1>

<p align="center"><strong>Generative Worlds for Adaptive Training of Bronchoscopy SLAM Models</strong></p>

<p align="center">
  <img src="https://geps.dev/progress/100" alt="100% complete">
</p>

![1000 Lungs conditional-line and modality header](docs/assets/1000lungs-header.png)

1000 Lungs is a synthetic virtual bronchoscopy dataset built using SynAirG, a
customisable model that generates synthetic worlds for bronchoscopy and general
endoluminal interventions.

1000 Lungs models are built from MAISI-generated chest CT volumes. Each
accepted case keeps the generated volume, paired labels, extracted airway mask,
airway mesh, centerline, procedure-style camera path, rendered condition
streams, and episode metadata under one case ID.

![1000 Lungs generation process diagram](docs/assets/synairg-pipeline.png)

- Episodes: 1000
- Task: navigate a flexible bronchoscope through synthetic human airways
- Modalities: depth video, normal video, PPS video, composite metadata review MP4, CT NIfTI, mesh geometry,
  source scope paths, TUM pose trajectory.
- RGB status: photorealistic RGB may be supplied by a later second sweep; geometry-first batches
  use an explicit unavailable placeholder in the composite review.
- Source: MAISI `rflow-ct` chest CT plus MAISI-generated paired labels.

## MAISI Instantiation

This dataset uses the SynAirG bulk MAISI path:

- Infer template: `maisi_thoracic_airway_infer.json`.
- Region: chest.
- Anatomy request: trachea, airway, left lung upper lobe, left lung lower lobe, right lung upper lobe, right lung middle lobe, right lung lower lobe, heart.
- Default output size: 512x512x256 voxels.
- Default spacing: 0.75x0.75x1.22656 mm.
- Source mask mode: none; the airway mask is extracted after generation from
  the paired MAISI label map.
- Airway labels: 57x132, resolved through MAISI
  output-label mapping when present.

## Build Process

For each accepted case, SynAirG:

1. Runs the MAISI-compatible `rflow-ct` wrapper to emit a chest CT and paired
   label map.
2. Extracts a binary airway mask from the label map.
3. Builds a bronchoscopy-ready airway mesh, centerline graph, branch metadata,
   and mesh-to-CT registration sidecar.
4. Samples a procedure-style virtual bronchoscope path.
5. Renders synchronized episode videos and condition streams.
6. Writes LeRobot/Open-H episode parquet, MP4 features, per-case medical
   metadata, TUM trajectory text, and pose JSONL.

## Conditioning for PBR-Based Inference

The exported depth, normal, PPS, and mask streams are aligned frame-for-frame
with the bronchoscope pose. They can be used as ControlNet-style conditioning
for PBR-based inference systems, including BronchoGen-style pipelines or custom
SynAirG renderers. The mesh and material-map artifacts can also drive a
geometry-preserving PBR pass before or alongside image-space generation.

## Synthetic Use Notice

Synthetic only. Not for clinical interpretation, clinical deployment,
autonomous diagnosis, or regulatory submission.

## Credits

- Generator: MAISI / MAISI-v2 `rflow-ct` provides the synthetic chest CT and
  paired label volumes.
- Format convention: the episode tree follows the Open-H-Embodiment /
  LeRobot v2.1 layout convention for paired video and kinematic data.

## Citation

```bibtex
@dataset{voncsefalvay_1000_lungs_2026,
  author       = {von Csefalvay, Chris},
  title        = {1000lungs: A Dataset of Generative Worlds for Adaptive Training of Bronchoscopy SLAM Models},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/chrisvoncsefalvay/1000lungs}},
  doi          = {10.57967/hf/9209}
}
```