scripts/gen-annotations/README.md

# Regenerating MedVision data

These scripts rebuild the preprocessed images/masks and the benchmark-plan annotations for
any dataset in the catalogue, from the original public sources. They are the record of how
the published data was actually produced.

They do **not** upload anything. Publishing to HuggingFace is a separate, deliberate step.

```
setup-env.sh        install medvision_ds
dataset_specs.py    per-dataset recipe: download entrypoint, steps, reorientation policy
build_dataset.py    the driver
```

## Quick start

```bash
./setup-env.sh /path/to/data-dir

# see what would run, without running it
python build_dataset.py --data_dir /path/to/data-dir --dataset KiTS23 --dry_run

# build it
python build_dataset.py --data_dir /path/to/data-dir --dataset KiTS23
```

`--data_dir` is your `MedVision_DATA_DIR`; datasets are built under `<data_dir>/Datasets/`.
Use `--all` for the whole catalogue, `--steps` to run a subset
(`download,segmentation,detection,biometry`), and `--max_workers` for the downloads that
support parallelism.

- `download`: data retrieval and preprocessing
- `segmentation`: generate `MaskSize` annotations
- `detection`: generate bounding box annotations
- `biometry`: generate tumor/lesion size or angle/distance annotations

## Two modes, and only two

**Reproduce what is published** — the default, also spelled `--latest`. Each
*(dataset, task)* pair (i.e., config) resolves to the newest version declared for it in
`_ANNOTATION_INDEX` (in `MedVision.py`), and that value is passed down as
`--annotation_version`. Config of one dataset can sit at different versions and are handled
in a single run: KiTS23 emits `1.0.0` for segmentation and detection and `1.1.1` for
biometry.

**Publish a new annotation version** — `--new-annotation-version`, for maintainers. Bump
`src/medvision_ds/__version__.py` first; the driver then passes no version and the planner
stamps `__version__`. The run is refused unless that version is **strictly above** every
affected pair's newest published version, so you cannot overwrite published data by
forgetting to bump.

There is no third mode. In particular **you cannot ask for an older version**, because the
current codebase cannot produce one: the generation code changes between annotation
versions — that is *why* the version is bumped. v1.1.0 changed the tumour/lesion
cluster-size threshold (200px → 20px), v1.1.1 corrected the transposed in-plane spacing in
the ellipse fit, v1.2.1 pinned float promotion against NEP 50. Naming an older version
would emit a file with that name but with today's values. To rebuild an older version,
check out the git commit with "[release]" in the commit title and install that `src/`.

## Credentials

Read from the environment only. Never put a token in a file that a script reads.

| Variable | Needed for | |
| --- | --- | --- |
| `SYNAPSE_TOKEN` | BCV15, BraTS24, FeTA24, MAMA-MIA | required; the download fails without it |
| `HF_TOKEN` | SKM-TEA, ToothFairy2 | only if their HuggingFace mirror is private for you |
| `MedVision_SKMTEA_HF_ID`, `MedVision_ToothFairy2_HF_ID`, `BiometricVQA_KiPA22_HF_ID` | those three | optional; override a working public default |

The driver checks the required ones before running anything and names the missing variable.

> **These tokens are for reproduction, not for loading.** Ordinary `load_dataset` use needs no
> credentials across most of the catalogue: 21 of the 30 datasets fetch an already-preprocessed
> copy, so nothing is retrieved from the gated source. BCV15, BraTS24 and MAMA-MIA are gated
> upstream but ship such a copy, and therefore load without a token.
>
> Three datasets are the exception, because no redistributable copy exists and the loader must
> build them from source at load time too:
>
> | Dataset | Also needed when loading |
> | --- | --- |
> | FeTA24 | `SYNAPSE_TOKEN` |
> | SKM-TEA, ToothFairy2 | access to their HuggingFace mirror — `HF_TOKEN` only if it is private for you |

## Reorientation

Ground truth is read in voxel space, so annotations must be computed against images that
are already in RAS+. Two places can do that, and `dataset_specs.py` records which one each
dataset uses:

- **8 datasets reorient inside their own `download_raw.py`** (AFIDs, DEEP-PSMA, LIDC-IDRI,
  LNQ2023, MAMA-MIA, PDDCA, PI-CAI, VerSe) and are given no `--reorient2RAS`. It has to
  happen there because AFIDs, PDDCA and VerSe derive landmark *voxel indices* at download
  time, and the landmark-biometry planner cannot reorient at all.
- **The rest are reoriented by the planner**, so `--reorient2RAS` is passed to every step
  that accepts it. Repeating it is free: the reorienter early-returns on an already-RAS
  file and writes nothing.

Ceph-Biometrics-400 receives the flag nowhere, which is correct rather than special-cased —
its only task is landmark biometry, whose planner has no such parameter, and its 2D X-rays
sit on an identity affine already.

`dataset_specs.py` carries the full rationale as comments.

## Tumour/lesion biometry: v1.1.0 and v1.1.1 are not reproduced here

For **KiTS23, BraTS24, MSD, autoPET-III, HNTSMRG24 and KiPA22** the newest biometry
annotation is `1.1.1`, and it was not produced by `preprocess_biometry.py`. Its lineage is:

| version | produced by |
| --- | --- |
| `1.0.0` | `preprocess_biometry.py` — what this driver runs |
| `1.1.0` | [`scripts/regenerate_tl_annotation_v1.1.0.py`](../regenerate_tl_annotation_v1.1.0.py) |
| `1.1.1` | [`scripts/regenerate_tl_annotation_v1.1.1.py`](../regenerate_tl_annotation_v1.1.1.py) + [`scripts/align_tl_split_to_v1.0.0.py`](../align_tl_split_to_v1.0.0.py) |

Those two regeneration scripts remain in `scripts/` and are described in
[`doc/release-v1.1.1.md`](../../doc/release-v1.1.1.md). They are deliberately **not** wired
into this driver: `align_tl_split_to_v1.0.0.py` rewrites already-published plans in place,
which is the practice the annotation-identity policy exists to prevent, and it should stay
a deliberate one-off rather than a routine step.

## Limitations worth knowing

- **Split parameters are fixed catalogue-wide, not per-dataset choices.** Random seed `1024`
  and split ratio `0.7` are the default in all 72 `preprocess_*.py`, and the same two values
  are declared as `RAMDOM_SEED` and `SPLIT_TRAIN_RATIO` in `MedVision.py`. The 11 tumour/lesion
  biometry scripts likewise all default to `--shrunken_bbox_scale 0.9` and
  `--enlarged_bbox_scale 1.1`. The driver overrides none of them, so a rebuild inherits exactly
  the constants the published data was built with — this is not an assumption to be recovered.
  Verified end to end: regenerating PI-CAI segmentation and detection into a scratch directory
  reproduces the published plans byte for byte, split membership and order included.
- **`--force_uint16_mask` is not passed.** Whether it was used per dataset is not
  recoverable; it is a mask-dtype hygiene step that does not change annotation values.
- **Downloads are large.** Several datasets are tens of gigabytes. `--dry_run` first.
- **Existing plan files are never overwritten** without `--force`.