README.md

---
license: cc-by-nc-sa-4.0
pretty_name: CAMUS
task_categories:
  - image-segmentation
tags:
  - medical-imaging
  - echocardiography
  - ultrasound
  - cardiac
  - left-ventricle
  - 2d-echo
size_categories:
  - 10K<n<100K
configs:
  - config_name: ed_es
    data_files:
      - split: train
        path: ed_es/train-*
      - split: validation
        path: ed_es/validation-*
      - split: test
        path: ed_es/test-*
  - config_name: half_sequence
    data_files:
      - split: train
        path: half_sequence/train-*
      - split: validation
        path: half_sequence/validation-*
      - split: test
        path: half_sequence/test-*
---

# CAMUS — Cardiac Acquisitions for Multi-structure Ultrasound Segmentation

2D transthoracic echocardiography from **500 patients** at the University Hospital of
St Etienne (GE Vivid E95, M5S probe). Each patient contributes an apical two-chamber
(**2CH**) and four-chamber (**4CH**) view. Segmented structures: **LV endocardium, LV
myocardium, left atrium**.

Converted from the official CREATIS release; see [Provenance](#provenance) for the exact
source items and retrieval date.

## Configs

| Config | Rows | On disk | Contents | GT |
|---|---|---|---|---|
| **`ed_es`** *(default)* | **2,000** | 186 MB | the ED and ES frames, 500 × 2 views × 2 phases | **manual** |
| `half_sequence` | **21,232** | 1.97 GB | 19,232 ED→ES cine frames **+** those same 2,000 manual frames | mixed |

For reference the source release is 3.83 GB of gzipped float32 NIfTI. Both are
already-compressed encodings, so the PNG/parquet figures above are the honest comparison
— the 4× saving is on the *uncompressed* array, not on disk.

**`ed_es` is exactly `half_sequence[gt_manual == True]`** — same schema, same rows. The
`gt_manual` column is the single switch between "hand-drawn" and "propagated".

| Selector | Rows | Use |
|---|---|---|
| `gt_manual == True` | 2,000 | scoring; identical to the `ed_es` config |
| `source == "half_sequence"` | 19,232 | the contiguous cine — order by `frame_index`, group by `patient_id`+`view` |

The 2,000 endpoint rows are deliberately duplicated into `half_sequence` so
both configs stand alone. The cine already begins on the true ED frame — the standalone
ED image is **byte-identical** to its cine frame — so a video model prompted on frame 0
is prompted on real ED either way. What the duplicate rows add is the *manual* mask at
those positions; the cine's own endpoint masks are a separate rasterization of the same
contours (Dice 0.998, not bitwise equal).

## Only ED and ES are hand-drawn

The intermediate cine masks are **propagated/interpolated from the two manual
endpoints**. This was measured, not assumed. Reconstructing each intermediate mask from
*only* its two endpoint masks by signed-distance-field blending gives:

| Target | CAMUS half-sequence | TED (fully manual, same cohort) | gap |
|---|---|---|---|
| `lv_endo` = `{1}` | **0.9943** | 0.9685 | +0.026 |
| `lv_epi` = `{1,2}` | **0.9957** | 0.9789 | +0.017 |

[TED](https://humanheart-project.creatis.insa-lyon.fr/ted.html) (Painchaud et al., IEEE
TMI 41(10), 2022) re-annotated **98 of these same patients** fully manually, frame by
frame, and is the control. Two checks make the gap readable: static no-motion baselines
are near-identical (0.8907 vs 0.8915), so CAMUS is not simply the easier cohort; and
downsampling TED 2× in y onto the CAMUS isotropic grid shifts it by **+0.0008**, so it
is not a resolution artifact. Even CAMUS's *worst* sequence (0.9869) beats TED's *mean*.

Practical reading: the residual is **~0.5%** on the scored targets — usable as a
temporal benchmark, but mid-cycle frames carry little independent annotation. **TED is
the fully-manual alternative** and is separately onboardable.

## Masks

**One label map per frame, raw upstream encoding, 8-bit grayscale PNG:**

| Value | Structure |
|---|---|
| 0 | background |
| 1 | LV endocardium (cavity) |
| 2 | LV myocardium |
| 3 | left atrium |

Verified across all 3,000 source GT files: every one contains exactly `{0,1,2,3}`.

> **In the Dataset Viewer the masks look almost black.** That is expected — the values
> really are 0–3 on an 8-bit scale, not empty masks.

Derive targets in the loader. **`lv_epi` must be the union `{1,2}`**, not bare label 2:
label 2 alone is a thin annulus, and scoring it directly measures rim geometry rather
than segmentation quality (bare label 2 drops to 0.80 on the same sequences where the
union scores 0.99). Masks are **not** pre-fanned-out into per-class binary columns here
because upstream is a single disjoint partition — unlike DRAC22 / iChallenge-PALM19,
where separate nullable columns exist because upstream shipped genuinely separate,
spatially overlapping mask files.

## Cardiac phase — and two broken cfg files

Frames are stored **in source order**; nothing was reordered at upload. `phase_direction`
carries the upstream declaration so the loader can normalize.

Direction was classified from `Info_*.cfg` by this predicate:

```
forward  <=>  ED == 1 and ES == NbFrame      981 view-exams
reverse  <=>  ES == 1 and ED == NbFrame       19 view-exams
```

That is the *declared* direction. It was then checked against the pixels using
**LV-cavity area** (`count(mask == 1)`), which must be larger at ED than at ES:

```
area(frame[ED]) > area(frame[ES])            998 / 1000 view-exams
```

Two exams fail it — `patient0185_2CH` and `patient0217_2CH`. Byte-matching all 2,000
standalone ED/ES images against every frame of their cine resolves what is actually
wrong: 1,996 match their cfg-declared index exactly, none fails to match some frame, and
all 4 mismatches are those two exams **with `ED` and `ES` transposed**. So the `.nii.gz`
files are correctly named; the **cfg index fields are swapped**. Both are flagged with
`cfg_phase_reliable = False`, and their `frame_index` values here are the verified ones.
Of the 21 cfgs *declaring* reverse order, **19 are genuinely ES→ED cines and 2 are
forward cines with transposed fields**.

## Splits

The official `subgroup_*.txt` files, verified to be a clean partition — pairwise
intersections all zero, union exactly the 500 patients:

| Split | Patients | `ed_es` rows | `half_sequence` rows |
|---|---|---|---|
| `train` | 400 | 1,600 | 17,006 |
| `validation` | 50 | 200 | 2,058 |
| `test` | 50 | 200 | 2,168 |

Splits are **patient-level**; always group on `patient_id` (both views and every frame of
a cine belong to one patient).

> Unlike the 2019 challenge distribution, **test ground truth is included** — CREATIS
> published the held-out masks in this NIfTI re-release after closing the online
> leaderboard. All three splits are real, official and fully labelled.

`information.txt` notes that "a few corrections have been made, resulting in slight
changes in distribution compared with the figures given in the article", so per-split
statistics will not reproduce the paper's tables exactly.

## Columns

| Column | Type | Notes |
|---|---|---|
| `image` | Image | 8-bit grayscale PNG, lossless (source is integral 0–255 in float32) |
| `mask` | Image | 8-bit grayscale PNG, values `{0,1,2,3}` |
| `patient_id` | string | `patient0001` … `patient0500` — **group on this** |
| `view` | string | `2CH` or `4CH` |
| `split` | string | `train` / `validation` / `test` |
| `source` | string | `endpoint` (manual) or `half_sequence` (cine) |
| `gt_manual` | bool | **True ⇔ hand-drawn.** `ed_es` == the True rows |
| `phase` | string \| null | `ED` / `ES`, null for mid-cycle cine frames |
| `frame_index` | int | 0-based position in the cine (verified, not merely declared) |
| `n_frames` | int | cine length for this view-exam (10–42, median 19) |
| `ed_index`, `es_index` | int | 0-based, **as declared by the cfg** — wrong for the 2 flagged exams |
| `phase_direction` | string | `forward` / `reverse`, as declared |
| `cfg_phase_reliable` | bool | False for `patient0185_2CH`, `patient0217_2CH` |
| `sex` | string | `M` (330) / `F` (170) |
| `age` | int | 18–93, median 67 |
| `image_quality` | string | `Good` / `Medium` / `Poor` — per view |
| `ef` | float | ejection fraction, 5–81, median 46 (patient-level; identical for both views) |
| `frame_rate` | float | Hz, 32.6–85.5 |
| `height`, `width` | int | 323–1181 × 292–973; 74 distinct sizes |
| `spacing_x`, `spacing_y` | float | uniformly 0.308 mm × 0.308 mm across all 1000 exams |

Per-patient image quality (worst of the two views) is Good 175 / Medium 231 / Poor 94,
reproducing the paper's 35 / 46 / 19 %. The **Poor** tier is where segmentation quality
actually separates — worth reporting as a stratified breakdown.

## Provenance

Built from the official CREATIS Girder deposit — **not** a third-party mirror. Several
circulating copies are defective: one hard-codes an invented ID-range split that puts all
50 official test patients into training, another ships labels 1 and 2 swapped, and
several relicense this NC-SA dataset as Apache-2.0, MIT or CC0.

| | |
|---|---|
| Release | **`CAMUS_public` NIfTI re-release** — *not* the 2019 `.mhd/.raw` challenge distribution |
| Girder collection | [`6373703d73e9f0047faa1bc8`](https://humanheart-project.creatis.insa-lyon.fr/database/api/v1/collection/6373703d73e9f0047faa1bc8) |
| `database_nifti` folder | `63fde55f73e9f004868fb7ac` |
| `database_split` folder | `66e27d12961576b1bad4e4e1` |
| Download URL | `https://humanheart-project.creatis.insa-lyon.fr/database/api/v1/collection/6373703d73e9f0047faa1bc8/download` |
| Retrieved | **2026-07-29** |
| Archive | 3,833,335,494 B, 7,508 files, 500 patient folders × 15 files |

The 2019 release differs materially — it was `.mhd/.raw`, split 450 train / 50 test with
**test GT withheld** and the test folder renumbered from 1 (so flattening the two folders
silently collides 50 IDs); its `_sequence` was the full cycle but *unannotated*; and its
grid was anisotropic uint8 (0.308 × 0.154 mm) rather than isotropic float32
0.308 × 0.308 mm. The two releases are not pixel-identical, which is why a dated item ID
is recorded here rather than just "CAMUS".

`manifest.csv` lists **sha256, byte size, array shape and dtype for all 6,000 original
`.nii.gz` files**; `manifest_aux.csv` covers the 1,508 text assets. Anyone can verify
their own CREATIS download is the exact input this parquet was converted from.

### Why the raw NIfTIs are not mirrored here

Deliberate, not an oversight. The pixel data round-trips **losslessly**: CAMUS stores
integral 0–255 values in float32, asserted frame-by-frame during conversion rather than
sampled. The 2D echo affine carries nothing beyond in-plane spacing, which is a column.
So re-hosting 3.8 GB of float32 would add bytes, not information — and
`manifest.csv` preserves byte-level verifiability regardless. `source_metadata/` carries
all 1,000 `Info_*.cfg` files, the split files, `information.txt`, the EF notebook, and
the license/citation texts.

## Contamination and overlap

- **CAMUS is very likely inside MedSAM's training corpus.** MedSAM's supplementary table
  lists CAMUS · Ultrasound · **21,232 pairs**, unstarred (training, not held-out) — and
  an independent inventory of this release gives 19,232 cine frames + 2,000 endpoint
  frames = **21,232 exactly**. MedSAM-family scores on CAMUS are therefore not a clean
  held-out measurement. CAMUS is also reported inside US30K/SAMUS, UltraSam,
  BiomedParseData, U2-BENCH and FedCVD.
- **TED** and **syntheticCAMUS** are derived from **98 of these 500 patients** and ship
  no cross-reference ID (both renumber to `patient001`–`patient098`). 94 of the 98 fall
  in the CAMUS *train* split and 4 in the official *test* split, so training on TED and
  testing on CAMUS leaks those 4.
- **No overlap with ACDC** — different modality (cine MRI), different hospital (Dijon),
  different cohort. The two share only a hosting portal and an author.
- No overlap with EchoNet-Dynamic (Stanford), CETUS (3D echo, multi-centre), HMC-QU
  (Doha), or the Medical Segmentation Decathlon (no echocardiography at all).

## License

**CC BY-NC-SA 4.0** — Attribution, NonCommercial, ShareAlike. This parquet conversion is
a derivative work, so **ShareAlike binds it too**: this repository is redistributed under
the same license, and so must anything derived from it.

The upstream `LICENSE_TERMS.md` adds two terms that travel with the data:

1. **Non-commercial scientific research use only.**
2. **Citation is mandatory** when referencing the dataset.

```bibtex
@article{leclerc2019camus,
  author  = {Leclerc, Sarah and Smistad, Erik and Pedrosa, Joao and {\O}stvik, Andreas
             and Cervenansky, Frederic and Espinosa, Florian and Espeland, Torvald and
             Berg, Erik Andreas Rye and Jodoin, Pierre-Marc and Grenier, Thomas and
             Lartizien, Carole and D'hooge, Jan and Lovstakken, Lasse and Bernard, Olivier},
  title   = {Deep Learning for Segmentation Using an Open Large-Scale Dataset in
             2D Echocardiography},
  journal = {IEEE Transactions on Medical Imaging},
  volume  = {38},
  number  = {9},
  pages   = {2198--2210},
  year    = {2019},
  doi     = {10.1109/TMI.2019.2900516}
}
```

## Source

- **Paper**: S. Leclerc, E. Smistad, J. Pedrosa, A. Østvik, et al. "Deep Learning for
  Segmentation using an Open Large-Scale Dataset in 2D Echocardiography." *IEEE TMI*
  38(9):2198–2210, 2019. doi:[10.1109/TMI.2019.2900516](https://doi.org/10.1109/TMI.2019.2900516)
- **Homepage**: https://www.creatis.insa-lyon.fr/Challenge/camus/
- **Data portal**: https://humanheart-project.creatis.insa-lyon.fr/database/