README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- image-segmentation
tags:
- medical
- mri
- cardiac
- heart
- myocardial-infarction
- delayed-enhancement
- late-gadolinium-enhancement
- lge
- psir
- left-ventricle
- emidec
- miccai-2020
pretty_name: EMIDEC - Myocardial Infarction from Delayed-Enhancement Cardiac MRI
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test_unlabeled
    path: data/test_unlabeled-*
dataset_info:
  features:
  - name: case_id
    dtype: string
  - name: case_name
    dtype: string
  - name: split
    dtype: string
  - name: pathology
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: overlay
    dtype: image
  - name: overlay_zoom
    dtype: image
  - name: preview_slice
    dtype: int32
  - name: n_slices
    dtype: int32
  - name: shape_xyz
    dtype: string
  - name: spacing_xyz
    dtype: string
  - name: slice_gap_mm
    dtype: float32
  - name: slice_gap_matches_pixdim
    dtype: bool
  - name: label_values
    dtype: string
  - name: cavity_voxels
    dtype: int64
  - name: myocardium_voxels
    dtype: int64
  - name: infarction_voxels
    dtype: int64
  - name: no_reflow_voxels
    dtype: int64
  - name: has_infarction
    dtype: bool
  - name: has_no_reflow
    dtype: bool
  - name: foreground_fraction
    dtype: float32
  - name: sex
    dtype: string
  - name: age
    dtype: int32
  - name: lvef_percent
    dtype: float32
  - name: troponin
    dtype: float32
  - name: killip_max
    dtype: int32
  - name: image_path
    dtype: string
  - name: mask_path
    dtype: string
  splits:
  - name: train
    num_bytes: 9314318
    num_examples: 100
  - name: test_unlabeled
    num_bytes: 1074810
    num_examples: 50
  download_size: 10402723
  dataset_size: 10389128
---

# EMIDEC — Myocardial Infarction from Delayed-Enhancement Cardiac MRI

The **MICCAI 2020 EMIDEC challenge**: segment the left ventricle and characterise
myocardial infarction in **delayed-enhancement (late gadolinium) cardiac MRI**.
Acquired at the **University Hospital of Dijon** (ImViA Lab, Université de
Bourgogne Franche-Comté).

Unlike most cardiac MRI benchmarks — which segment anatomy on **cine** images —
EMIDEC targets **tissue pathology** on a static DE-MRI stack: normal myocardium
vs. infarcted myocardium vs. the no-reflow (microvascular obstruction) core.

## What this mirror contains — read first

> **The test set has no ground truth, and never did publicly.** The official
> release ships 100 training cases *with* masks and 50 test cases *with images
> only* — the organizers withheld test GT for the leaderboard and scored
> submissions by email. This is confirmed structurally in the archive (zero
> `Contours/` directories in the test archive) and in the test archive's own
> `Readme.txt`. The 50 test cases are mirrored here as `test_unlabeled` for
> completeness; **they cannot be used to compute Dice.**

> **Two different test sets exist upstream.** The segmentation and classification
> contests did *not* use the same 50 patients ("the cases are not the same for
> the two contests"; cases were added and removed for the classification split).
> This mirror carries the **segmentation** test set.

> **Beware third-party mirrors.** Seven Kaggle/HF re-uploads exist. All mislabel
> the license (MIT / Apache-2.0 instead of CC BY-NC-SA 4.0), and the HuggingFace
> one (`viennh2012/cardiac_cine_emidec`) is a **corrupted derivative**: images
> per-slice min–max normalised to uint8 (every slice max becomes exactly 254,
> destroying the cross-slice intensity relationship that *is* the DE-MRI signal),
> masks damaged by interpolated resampling, and the 100 training cases re-split
> into its own train/val/test — so its "test" set is official *training* data.
> It is also misnamed "cine"; EMIDEC is not cine. Use this mirror or emidec.com.

## Dataset Details

| Field | Value |
|---|---|
| Modality | Delayed-enhancement cardiac MRI (DE-MRI / LGE), T1-weighted **PSIR**, phase-sensitive image only |
| Acquisition | ~10 min after Gd-DTPA 0.1–0.2 mmol/kg; TR 3.5 ms, TE 1.42 ms, TI 400 ms, flip 20°; ECG-gated, breath-hold |
| Scanners | Siemens Aera 1.5 T and Skyra 3 T |
| Body part | Heart — **left ventricle only**, short-axis base→apex (RV is not annotated) |
| Cases | **150 patients** — 100 train (33 normal + 67 pathological, with GT) + 50 test (images only) |
| Slices | **708** across the 100 training cases; **4–10 per case** |
| In-plane | 1.367–1.875 mm; **92 distinct in-plane shapes** across 100 cases |
| Slice gap | **8.0 / 8.9 / 9.6 / 10.0 / 13.0 / 13.04 mm** — 10 mm in only 66 of 100 cases |
| Format | `.nii.gz`; images `float64`, masks `uint8` (86) / `uint16` (14) |
| Intensity | Integral 12-bit values, global range **0–4095**, per-case max 3684–4095 (no upstream normalisation) |
| License | **CC BY-NC-SA 4.0** — stated on emidec.com *and inside the archives* |
| Paper | Lalande et al., *Data* **2020**, 5(4):89 · doi:10.3390/data5040089 |

## Label encoding

`0` background · `1` cavity · `2` normal myocardium · `3` infarction · `4` no-reflow (PMO/MVO)

Papillary muscles are **included in the cavity** (label 1, not myocardium). The
epicardial border excludes fat, and the contour is extrapolated across the
junction of the two ventricles.

> **⚠️ The archives' own `Readme.txt` lists the classes in the wrong order.** Its
> prose reads *"background, myocardium, cavity, myocardial infarction and
> no-reflow"*, which implies `1`=myocardium and `2`=cavity. That is **wrong** —
> it contradicts the organizers' `Evaluation-metrics` code
> (`{"background":0,"cavity":1,"normal_myocardium":2,"infarction":3,"NoReflow":4}`).
> Verified here from the voxels across all 100 training masks: label `1` sits at
> mean in-plane radius **10.3 px** from the heart centroid while label `2` sits at
> **18.8 px**, and in **432 slices** more than half of label `1` falls inside the
> filled holes of label `2` (zero slices vote the other way). Label `2` is the
> ring; label `1` is the blood pool. **The eval code is right, the Readme prose is
> not.**

### ⚠️ Evaluation targets are nested unions, not one-vs-rest

Straight from the organizers' `main.py`. Getting this wrong is the most common
EMIDEC mistake:

| Target | Label set | Cases with non-empty GT (of 100) |
|---|---|---|
| `cavity` | `{1}` | 100 |
| `myocardium` | **`{2, 3, 4}`** | 100 |
| `infarction` | **`{3, 4}`** | 67 |
| `no_reflow` | `{4}` | 40 |

The no-reflow core is *inside* the infarct, and the infarct is *inside* the
myocardial wall. Reported infarct **volumes** likewise include PMO. Dice is
computed **in 3D over the whole volume**, not averaged per slice.

### Verified label properties (measured on all 100 training masks)

- Only **three** label sets occur: `{0,1,2}` ×33, `{0,1,2,3}` ×27, `{0,1,2,3,4}` ×40.
  Always a contiguous prefix — there is **no non-contiguous-label problem**.
- The 33 cases with `{0,1,2}` are **exactly** the 33 normal (`N`) cases. Labels
  `3` and `4` are absent from every normal case.
- Label `3` present in **67/100** (all pathological), label `4` in **40/100**.
- Label `4` is tiny: **13–2976 voxels** per case (median 88). Expect near-zero
  no-reflow Dice from any naive model.
- Foreground is **2.92 %** of voxels on average (range 1.30–5.54 %).
- **All 708 slices contain foreground** — only slices with visible myocardium
  were released, so the stack is not full-thorax coverage.

## ⚠️ Spacing lives ONLY in pixdim — the affine does not encode it

**The single easiest thing to get wrong with this dataset.**

Every one of the 100 training images has:

```
sform_code = 2   (ALIGNED_ANAT — i.e. declared valid)
qform_code = 0
affine     = diag(-1, -1, 1)      # LPS axcodes, zero translation, NO SCALE
pixdim     = (1.367-1.875, same, 8.0-13.04)   # the real spacing
```

The NIfTI spec says to use the sform when `sform_code > 0`, so **any code that
reads spacing straight off the affine gets 1 mm isotropic** and silently mis-scales
every physical-unit result: infarct volume, Hausdorff distance, and any resampling.
With a true slice gap of 8–13 mm that is an order-of-magnitude error through-plane.
The common form of this bug is `np.diag(nib.load(p).affine)[:3]`, or anything
deriving spacing from `affine[:3,:3]`.

**The two major toolkits both handle it correctly**, so this is a hazard for
hand-rolled readers rather than for MONAI/ITK pipelines — verified on this mirror:

| Reader | Result |
|---|---|
| `nibabel` raw `.affine` | `diag(-1, -1, 1)` → **wrong**, 1 mm isotropic |
| `nibabel` `header.get_zooms()` | `(1.5625, 1.5625, 10.0)` → correct |
| MONAI `LoadImaged` / `NibabelReader` | rebuilds the affine from pixdim → `diag(-1.5625, -1.5625, 10.0)`, **correct** |
| `SimpleITK` | warns `has unexpected scales in sform`, then recovers from pixdim → correct |

**These files are mirrored byte-identically and the header is deliberately NOT
patched**, so this mirror stays comparable to the official release and to
published EMIDEC results. Instead, the true spacing is recorded per case in
`train.jsonl` / `test_unlabeled.jsonl` (`spacing_xyz`, `slice_gap_mm`). Read
spacing from `header.get_zooms()` or from the jsonl — never from the affine.

## Geometry: the heart is centred but tiny — and *not* cropped

A widely repeated claim says EMIDEC images are cropped around the heart. **They
are not.** The field of view is full-thoracic (e.g. 216 × 359 mm) and the heart
occupies only ~2–5 % of the in-plane area. What upstream actually did is
**in-plane re-registration**: *"the slices are realigned according to the gravity
centre of the area defined by the epicardial contour"* — so the foreground
centroid sits essentially on the image centre.

That is why a **centre crop is the safe preprocessing choice** here, and why
every challenge entrant used one. It is a consequence of the re-centering, not of
any upstream cropping.

Measured on this mirror: a **144 × 144 centre crop of the middle slice retains
100 % of the ground-truth foreground in all 100 training cases** — not a single
case loses a voxel. So a fixed centre crop is not merely conventional here, it is
lossless at that size.

## ⚠️ Possible patient overlap with ACDC — unverifiable

EMIDEC and **ACDC** share a great deal: the same hospital (CHU Dijon), the same
senior author, the **same two annotators** described identically ("two independent
experts, 10 and 20 years of experience, reaching consensus"), the same *n* = 150,
the same 100/50 split, and the same Siemens Aera 1.5 T. Decisively, EMIDEC was
extracted from *"a conventional cardiovascular exam [that] included cine-MRI and
DE-MRI"* — the very exam family ACDC's cine images come from. ACDC also contains
a 30-patient **MINF** group (prior myocardial infarction).

**Neither release carries a cross-reference ID**, both are fully anonymised with
DICOM headers stripped, and acquisition years are not published — so overlap
**cannot be checked or excluded by any downstream user**, and no xref column
could be preserved in this mirror.

Partial mitigation: EMIDEC is **acute** MI (imaged within ~1 month of
angioplasty) with pathological LVEF **47.7 ± 13.2 %**, mostly *above* ACDC's
MINF threshold of < 40 %; and EMIDEC's 3 T cases used a Skyra where ACDC used a
Trio Tim, suggesting a later acquisition window.

**Do not place EMIDEC and ACDC on opposite sides of a train/test split** in a
combined cardiac benchmark, and caveat any claim of independence between them.

No overlap with **MyoPS 2020 / MyoPS++ / MS-CMRSeg** (Shanghai Renji cohort),
**LAScarQS** (left atrium), **CMRxMotion** (Fudan volunteers), or **M&Ms**
(multi-centre ES/DE/CA) — different institutions and cohorts; author overlap only.

## Ground truth

A single gold-standard tier, which is also the only mask released:

| Pass | Who | Role |
|---|---|---|
| 1 | Cardiologist, 10 yr experience | Drew all contours manually in **QIR** (CASIS, Quetigny) |
| 2 | Biophysicist, 20 yr cardiovascular MRI | *"went through every outline and made some changes when necessary"* |

The expert-2-revised contours are the leaderboard reference. Manual (not
semi-automatic) was deliberate: *"questionable contours ... are only due to the
choice of the experts and do not depend on algorithm settings."*

**Human ceiling** (measured by the authors on 34 *other* cases, not released):

| | Myocardium | Myocardial infarction |
|---|---|---|
| Intra-observer Dice | 0.84 | 0.76 |
| Inter-observer Dice | 0.83 | 0.69 |

A model at infarct Dice ≈ 0.7 is already at inter-observer level. No multi-rater
masks were released for the 150 cases.

## Structure

```
train/images/Case_XXXX.nii.gz            # 100 DE-MRI volumes  (N###/P###)
train/masks/Case_XXXX.nii.gz             # 100 masks, same grid, values 0-4
test_unlabeled/images/Case_NNN.nii.gz    #  50 volumes, 101-150, NO masks exist
train.jsonl                              # per-case metadata
test_unlabeled.jsonl
clinical_metadata.csv                    # all 150 cases, 13 clinical fields
README.md
LICENSE.txt
```

Case IDs are the official ones. Train uses `Case_N###` / `Case_P###` where the
number is a **global** sequence 001–100 and the letter is the class; test uses
`Case_101`–`Case_150`.

> **The `N`/`P` prefix leaks the pathology label.** It is retained for fidelity
> with the official release and the leaderboard, but a classifier that reads case
> filenames scores 100 % locally and 50 % on the real (numerically-named) test
> set. Use the `pathology` column deliberately, not the filename.

### The parquet preview layer is display-only

`data/*.parquet` exists so the HF Dataset Viewer can render this dataset. Each row
holds the **middle slice** of one volume as PNG: `image` (full-FOV, grayscale),
`mask` (class-coloured), `overlay`, and `overlay_zoom` (the 144 × 144 centre crop,
upscaled). Colours are `1` cavity blue, `2` normal myocardium green, `3` infarction
yellow, `4` no-reflow red.

> **Do not train or evaluate on the preview.** Its intensities are
> percentile-windowed to 8-bit for display and it holds one slice per case. Treating
> a rendered preview as the data is exactly the error that makes the third-party
> mirror unusable. The real data is the byte-identical `.nii.gz` at the repo root.

For `test_unlabeled`, `mask` / `overlay` / `overlay_zoom` / `pathology` and every
label-derived column are `null`, because no test ground truth exists.

`train.jsonl` / `test_unlabeled.jsonl` columns:

| Column | Meaning |
|---|---|
| `case_id` | `"N006"` … `"P100"` (train), `"101"` … `"150"` (test) |
| `case_name` | `"Case_N006"` — the official directory name |
| `image`, `mask` | repo-relative paths (`mask` is `null` for `test_unlabeled`) |
| `split` | `"train"` or `"test_unlabeled"` |
| `pathology` | `"normal"` or `"pathological"` (train only; `null` for test) |
| `n_slices`, `shape_xyz` | geometry — **92 distinct in-plane shapes** |
| `spacing_xyz`, `slice_gap_mm` | **true spacing, from pixdim** (not the affine) |
| `slice_gap_mm_declared` | gap as written in the clinical txt — **disagrees with pixdim in 6/100 cases** |
| `slice_gap_matches_pixdim` | `false` for those 6 |
| `axcodes`, `sform_code`, `qform_code` | header provenance for the spacing caveat |
| `image_dtype`, `mask_dtype` | `float64`; `uint8` or `uint16` |
| `intensity_min`, `intensity_max` | per-case (never renormalised) |
| `label_values` | sorted labels present, e.g. `[0,1,2,3]` |
| `label_voxels` | `{label: voxel_count}` for `0`–`4` |
| `target_voxels` | `{cavity, myocardium, infarction, no_reflow}` under the **nested** unions |
| `has_infarction`, `has_no_reflow` | booleans |
| `foreground_fraction` | fraction of voxels with label > 0 |
| `clinical` | the 13 parsed clinical fields (see below) |

## Clinical metadata

Every case — all 100 train and all 50 test — ships a clinical text file, and all
13 fields are populated in **150/150** cases. Parsed into `clinical_metadata.csv`
and the `clinical` object in the jsonl.

| Field | Type | Domain over all 150 |
|---|---|---|
| `sex` | categorical | M 89, F 61 |
| `age` | int | 27–89 |
| `tobacco` | int | `1` ×53, `2` ×32, `3` ×65 — **see caveat** |
| `overweight` (BMI > 25) | bool | Y 84, N 66 |
| `arterial_hypertension` | bool | Y 61, N 89 |
| `diabetes` | bool | Y 20, N 130 |
| `familial_history_cad` | bool | Y 14, N 136 |
| `ecg_st_elevation` | bool | Y 95, N 55 |
| `troponin` | float (ng/mL) | 0.1–420 |
| `killip_max` | int | 1 ×121, 2 ×23, 3 ×3, 4 ×3 |
| `lvef_percent` | float (%) | 20–70 — **echocardiographic**, field is labelled `FEVG` |
| `ntprobnp` | float (pg/mL) | 3–22577 |
| `slice_gap_mm_declared` | float (mm) | 8 ×5, 10 ×120, 13 ×25 |

Parsing gotchas handled here, all of which bite a naive reader:

- The text files are **ISO-8859-1 with CRLF**, not UTF-8, and several keys carry a
  stray non-ASCII byte before the colon (`Gap between slices :`).
- Filenames use a **space** (`Case N058.txt`) while the image directories use an
  **underscore** (`Case_N058/`) — joining them naively fails.
- One Troponin value uses a **French decimal comma** (`4,5`) and must be
  normalised before `float()`.
- Each archive also contains a `Readme.txt` at top level that is **not** a case.
- `tobacco` is provided as a bare `1`/`2`/`3`. The descriptor paper describes this
  as yes / no / former, but **the mapping is not stated in the data itself**, so
  the raw integer is preserved here rather than a guessed decoding.
- **`slice_gap_mm_declared` disagrees with the NIfTI pixdim in 6 of 100 training
  cases** (`N070` 10→8.9, `P008` 10→9.6, `P043` 13→10.0, `P059` 10→8.0, `P095`
  10→13.0, `P097` 10→13.0). The clinical text is a hand-entered note; **pixdim is
  authoritative for geometry.**

## Source & Citation

- Official: https://emidec.com/dataset — CC BY-NC-SA 4.0. The site asks for a free
  self-service account; the archives themselves are served by an unauthenticated
  form POST.
- Organizers' evaluation code (the authority on label values and the nested eval
  groups): https://github.com/EMIDEC-Challenge/Evaluation-metrics
- Challenge design: https://doi.org/10.5281/zenodo.3755234 (PDF only — the data is
  **not** on Zenodo)

```bibtex
@article{lalande2020emidec,
  author  = {Lalande, Alain and Chen, Zhihao and Decourselle, Thomas and
             Qayyum, Abdulkadir and Pommier, Thibaut and Lorgis, Luc and
             de la Rosa, Ezequiel and Cochet, Alexandre and Cottin, Yves and
             Ginhac, Dominique and Salomon, Michel and Couturier, Raphael and
             Meriaudeau, Fabrice},
  title   = {Emidec: A Database Usable for the Automatic Evaluation of
             Myocardial Infarction from Delayed-Enhancement Cardiac MRI},
  journal = {Data},
  volume  = {5},
  number  = {4},
  pages   = {89},
  year    = {2020},
  doi     = {10.3390/data5040089}
}

@article{lalande2022deep,
  author  = {Lalande, Alain and Chen, Zhihao and Pommier, Thibaut and
             Decourselle, Thomas and Qayyum, Abdulkadir and Salomon, Michel and
             Ginhac, Dominique and Skandarani, Youssef and Boucher, Arnaud and
             Brahim, Khawla and de Bruijne, Marleen and others},
  title   = {Deep learning methods for automatic evaluation of delayed
             enhancement-MRI. The results of the EMIDEC challenge},
  journal = {Medical Image Analysis},
  volume  = {79},
  pages   = {102428},
  year    = {2022},
  doi     = {10.1016/j.media.2022.102428}
}
```