---
pretty_name: MSD Cardiac (Task02_Heart)
language:
- en
license: cc-by-sa-4.0
tags:
- medical
- mri
- cardiac
- segmentation
- left-atrium
- nifti
task_categories:
- image-segmentation
size_categories:
- 10-100
configs:
- config_name: default
  data_files:
  - split: train
    path: train.csv
  - split: test
    path: test.csv
---

# MSD Cardiac — Task02_Heart (Left Atrium Segmentation)

Processed NIfTI data from the **Medical Segmentation Decathlon** Task02 (Heart).
The goal is to segment the **left atrium** from mono-modal MR images.

## Dataset Summary
- **Modality**: MRI
- **Task**: Left atrium segmentation
- **Patients**: 30 total (20 train, 10 test)
- **Labels**: 0 = background, 1 = left atrium
- **Splits**: `train` (with labels), `test` (images only, no public labels)

## Data Structure (per patient)
Each patient directory contains:
- `<pid>.nii.gz` — MR image volume
- `<pid>_gt.nii.gz` — segmentation mask (train only)

## Columns
| Column | Type | Description |
|---|---|---|
| `pid` | string | Patient ID (e.g., la_003) |
| `image` | string | Relative path to MR image |
| `label` | string | Relative path to segmentation mask (None for test) |
| `orig_spacing_x` | float | Original X spacing (mm) |
| `orig_spacing_y` | float | Original Y spacing (mm) |
| `orig_spacing_z` | float | Original Z spacing (mm) |
| `n_slices` | int | Number of slices after resampling |
| `la_volume_cm3` | float | Left atrium volume (cm³, train only) |
| `la_proportion` | float | Left atrium voxel proportion (train only) |

## Resolution Details
| Statistic | Spacing (mm) | Size |
|---|---|---|
| min | (1.25, 1.25, 1.37) | (320, 320, 90) |
| median | (1.25, 1.25, 1.37) | (320, 320, 115) |
| max | (1.25, 1.25, 1.37) | (320, 320, 130) |

## Usage

```python
import pandas as pd
import nibabel as nib

df = pd.read_csv("train.csv")
row = df.iloc[0]
img = nib.load(row["image"])
arr = img.get_fdata()
```

## Source
Official MSD website: http://medicaldecathlon.com/

## License
CC-BY-SA 4.0

## Citation
```bibtex
@article{antonelli2022medical,
  title={The Medical Segmentation Decathlon},
  author={Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and others},
  journal={Nature Communications},
  year={2022},
  doi={10.1038/s41467-022-30695-9}
}
```