README.md

---
license: apache-2.0
pretty_name: iTrialSpace Lung Nodule Dataset
tags:
  - medical
  - medical-imaging
  - lung
  - ct
  - lung-nodule
  - segmentation
  - synthetic-data
  - digital-twin
  - vision-language
size_categories:
  - n>1T
task_categories:
  - image-segmentation
  - image-classification
  - visual-question-answering
---

# iTrialSpace — Lung Nodule Dataset

A multi-part lung-nodule resource spanning **real-CT segmentation masks**, **nodule metadata/profiles**, large-scale **synthetic CT volumes** (digital-twin / in-silico trial "modes"), and a **vision-language evaluation set** of axial QC images.

> ⚠️ **Large dataset.** The repository totals several terabytes. Most folders are plain files; the `vlm_dataset/synthetic/` images are packed into **tar shards** (see [below](#vlm_datasetsynthetic--tar-shards)). Use targeted downloads — pull only the paths you need.

## Repository layout

```
.
├── profiles/            # per-nodule profile tables (7 CSVs)
├── meta/                # per-dataset metadata tables (7 CSVs)
├── masks/               # real-CT segmentation masks (.nii.gz), 7 source datasets
├── generated_cts/       # synthetic CT volumes + masks, 13 generation "modes"
├── inserted_masks/      # inserted-nodule masks (.nii.gz), same 13 modes
└── vlm_dataset/
    └── synthetic/       # axial QC PNGs for VLM eval, packed as 27 tar shards
```

Source datasets referenced throughout: **DLCS24, IMDCT, LNDbv4, LUNA16, LUNA25, LUNGx, NSCLCR**.

---

## `profiles/` — nodule profile tables

Seven CSVs (one per source dataset), e.g. `LUNA25_nodule_profiles.csv`. Per-nodule descriptors.

## `meta/` — dataset metadata tables

Seven CSVs (one per source dataset), e.g. `LUNA25_dataset_FEB192026_FIT_v1.csv`. Case/series-level metadata.

## `masks/` — real-CT segmentation masks

NIfTI masks (`*.nii.gz`) for the 7 source datasets, **41,804 files total**. Each dataset contains several mask types as subfolders (e.g. `nodule_seg/`, `organ_seg/`, `refined_seg/`, `combined_seg/`, `radiomics_seg/`).

| Dataset | Mask files |
|---|---|
| LUNA25 | 18,363 |
| IMDCT | 10,127 |
| DLCS24 | 7,312 |
| LUNA16 | 2,985 |
| LNDbv4 | 1,461 |
| NSCLCR | 1,263 |
| LUNGx | 293 |
| **Total** | **41,804** |

## `generated_cts/` — synthetic CT volumes (in-silico trial "modes")

Synthetic CT volumes with paired masks and per-case JSON, organized into **13 generation modes**, **267,882 files total (~3 TB)**. Each case folder typically contains `synthetic_ct.nii.gz`, `input_mask*.nii.gz`, and metadata JSON (`dataset.json`, `pipeline_summary.json`, `nodmaisi_audit.json`).

| Mode | Files |
|---|---|
| mode1_controlled_prevalence | 5,057 |
| mode2_size_detection_curve | 3,003 |
| mode3_location_sensitivity | 2,510 |
| mode4_demographic_stratification | 4,813 |
| mode5_counterfactual | 15,081 |
| mode6_cross_dataset | 9,029 |
| mode7_bootstrap_confidence | 24,047 |
| mode8_algorithm_comparison | 3,011 |
| mode9_screening_simulation | 9,021 |
| mode10_multi_nodule_realism | 2,999 |
| mode11_digital_twin_isolation | 78,134 |
| mode12_digital_twin_complete | 54,099 |
| mode13_digital_twin_cross | 57,078 |
| **Total** | **267,882** |

<!-- TODO: add a sentence describing what each mode represents (the names are indicative; fill in exact semantics). -->

## `inserted_masks/` — inserted-nodule masks

NIfTI masks (`*_mask.nii.gz`) marking the inserted/synthesized nodules used to generate the
`generated_cts/` volumes — **one mask per case**, organized into the **same 13 modes**.
**45,018 files total (~130 GB)**, plus a small JSON per mode. Filenames encode provenance, e.g.
`iTS--<run>--C0252--host-<hostcase>--src-<source_dataset>--nod-<nodule_id>_mask.nii.gz`.

| Mode | Files |
|---|---|
| mode1_controlled_prevalence | 1,001 |
| mode2_size_detection_curve | 606 |
| mode3_location_sensitivity | 505 |
| mode4_demographic_stratification | 804 |
| mode5_counterfactual | 2,505 |
| mode6_cross_dataset | 1,505 |
| mode7_bootstrap_confidence | 4,020 |
| mode8_algorithm_comparison | 501 |
| mode9_screening_simulation | 1,503 |
| mode10_multi_nodule_realism | 502 |
| mode11_digital_twin_isolation | 13,094 |
| mode12_digital_twin_complete | 9,010 |
| mode13_digital_twin_cross | 9,462 |
| **Total** | **45,018** |

---

## `vlm_dataset/synthetic/` — tar shards

Axial QC images used for vision-language evaluation: **1,004,235 files** (~1,004,176 PNGs + summary CSV/JSON). To keep the repo healthy, these are packed into **27 tar shards**, organized by **model × mode**.

```
vlm_dataset/synthetic/
├── lung_axial_mode1.tar … lung_axial_mode13.tar                     # 13 shards
├── lung_axial_medgemma_mode1.tar … lung_axial_medgemma_mode13.tar   # 13 shards
├── misc.tar                                                         # 59 summary csv/json
└── _file_manifest.txt.gz                                            # full file → list index
```

- **Two model groups**: `lung_axial` and `lung_axial_medgemma`.
- **One shard per (model, mode)** — 13 modes each → 26 shards, plus `misc.tar`.
- Paths are **preserved inside the tars**, so extraction reconstructs the original tree
  (`lung_axial/qc_overlays/qc_mode7_…_C0085.png`, etc.).
- `_file_manifest.txt.gz` lists every original file path (one per line).

Approx. files per (model, mode) shard (lung_axial and lung_axial_medgemma are symmetric):

| Mode | files/shard | Mode | files/shard |
|---|---|---|---|
| mode1 | 11,998 | mode8 | 5,988 |
| mode2 | 7,200 | mode9 | 17,940 |
| mode3 | 6,000 | mode10 | 5,887 |
| mode4 | 9,588 | mode11 | 152,424 |
| mode5 | 29,952 | mode12 | 91,311 |
| mode6 | 18,000 | mode13 | 97,982 |
| mode7 | 47,818 | | |

### How to download and extract a shard

```bash
# one shard
huggingface-cli download TusharLab/iTrialSpace_Lung \
  vlm_dataset/synthetic/lung_axial_mode7.tar \
  --repo-type dataset --local-dir .

tar -xf vlm_dataset/synthetic/lung_axial_mode7.tar      # restores lung_axial/.../qc_mode7_*.png
```

```python
# Python: download + extract
from huggingface_hub import hf_hub_download
import tarfile

p = hf_hub_download(
    "TusharLab/iTrialSpace_Lung",
    "vlm_dataset/synthetic/lung_axial_medgemma_mode11.tar",
    repo_type="dataset",
)
with tarfile.open(p) as t:
    t.extractall("synthetic_extracted/")
```

```python
# stream a shard with WebDataset (no full extraction)
import webdataset as wds
url = "https://huggingface.co/datasets/TusharLab/iTrialSpace_Lung/resolve/main/vlm_dataset/synthetic/lung_axial_mode3.tar"
ds = wds.WebDataset(url)
for sample in ds:
    key = sample["__key__"]          # original relative path (minus extension)
    img = sample.get("png")          # PNG bytes
    break
```

---

## General download tips

```bash
# whole repo (very large — usually not what you want)
huggingface-cli download TusharLab/iTrialSpace_Lung --repo-type dataset

# just one folder, e.g. masks for one dataset
huggingface-cli download TusharLab/iTrialSpace_Lung \
  --repo-type dataset --include "masks/LUNA25/**"
```

## Notes & provenance

- Synthetic CT volumes (`generated_cts/`), inserted-nodule masks (`inserted_masks/`), and synthetic VLM images (`vlm_dataset/synthetic/`) are **machine-generated**; real-CT-derived assets live under `masks/`, `meta/`, `profiles/`.
- File counts above were verified at upload time: `masks/` = 41,804; `generated_cts/` = 267,882; `inserted_masks/` = 45,018; `vlm_dataset/synthetic/` = 1,004,235 files packed into 27 tar shards.

<!-- TODO (maintainers): fill in the following -->
- **Intended use / scope:** _TODO_
- **Licensing of underlying source datasets** (LUNA16/25, LNDb, LUNGx, NSCLC-Radiomics, etc.): _confirm per-source terms — the apache-2.0 above applies to this packaging, not necessarily to third-party source data._
- **Contact:** Fakrul Islam Tushar (first author) — fitushar@arizoan.edu · [Tushar Laboratory](https://tusharlabratory.github.io/)

## Citation

If you use this dataset, please cite:

```bibtex
@article{tushar2026itrialspace,
  title={iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models},
  author={Tushar, Fakrul Islam and Momy, Umme Hafsa and Lo, Joseph Y and Rubin, Geoffrey D},
  journal={arXiv preprint arXiv:2605.05761},
  year={2026}
}
```