Datasetpublic

viennh2012/cardiac_cine_msd

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… See the full description on the dataset page: https://huggingface.co/datasets/viennh2012/cardiac_cine_msd.

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Dataset Card

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}
}