Datasets:
File size: 5,435 Bytes
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dataset_info:
features:
- name: organ
dtype: string
- name: image
dtype: image
- name: binary_mask
dtype: image
- name: classes_mask
dtype: image
- name: volume_id
dtype: int32
- name: slice_id
dtype: int32
splits:
- name: '244'
num_bytes: 1287692805
num_examples: 51891
- name: '512'
num_bytes: 4640495381
num_examples: 51891
download_size: 5872933096
dataset_size: 5928188186
configs:
- config_name: default
data_files:
- split: '244'
path: data/244-*
- split: '512'
path: data/512-*
license: cc-by-sa-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- medical
- xray
- nii
- ct
- MRI
pretty_name: Mini Medical Segmentation Decathlon
size_categories:
- 100K<n<1M
---
# Processed and Reduced Medical Segmentation Decathlon Dataset
<!-- Provide a quick summary of the dataset. -->
The miniMSD dataset is a medical image segmentation benchmark covering 10 human organs.
It is derived from the [Medical Segmentation Decathlon (MSD)](http://medicaldecathlon.com) by converting volumetric scans
from NIfTI (NII) format into serialised 2D RGB images, alongside their corresponding segmentation masks.
The dataset is provided in multiple resolution variants (244, 512), enabling easier use,
off-the-shelf accessibility, and flexible experimentation.
## Dataset Details
The dataset covers 10 human body organs, listed below.
Each organ includes up to 40 volumes, with each volume consisting of a variable number of image slices.
Each dataset entry contains the following components: the organ type, the image, a binary mask,
a detailed (multi-class) mask, a volume ID, and a slice ID.
The image, binary mask, and detailed mask are all provided as PIL images.
The binary mask contains two labels: 0 for background and 1 for the target region.
The detailed mask contains multiple labels (0, 1, 2, 3, …), where each label corresponds to a specific
anatomical structure. The mapping of label indices to structures is provided below.
| Organ | Number of Volumes | Total Slices | Avg. Slices per Volume | % of Total Slices |
|----------------|-------------------|--------------|------------------------|-------------------|
| Prostate | 32 | 1204 | 37.625 | 1.26% |
| Heart | 20 | 2271 | 113.550 | 2.38% |
| Hippocampus | 40 | 2754 | 68.850 | 2.89% |
| HepaticVessel | 40 | 5796 | 144.900 | 6.08% |
| BrainTumour | 40 | 6200 | 155.000 | 6.51% |
| Spleen | 40 | 6964 | 174.100 | 7.31% |
| Pancreas | 40 | 7068 | 176.700 | 7.42% |
| Colon | 40 | 7344 | 183.600 | 7.71% |
| Lung | 40 | 22510 | 562.750 | 23.62% |
| Liver | 40 | 33200 | 830.000 | 34.83% |
## Labels Mapping
### BrainTumour
- 0: background
- 1: necrotic / non-enhancing tumor
- 2: edema
- 3: enhancing tumor
### Heart
- 0: background
- 1: left atrium
### Liver
- 0: background
- 1: liver
- 2: tumor
### Hippocampus
- 0: background
- 1: anterior
- 2: posterior
### Prostate
- 0: background
- 1: peripheral zone
- 2: transition zone
### Lung
- 0: background
- 1: nodule
### Pancreas
- 0: background
- 1: pancreas
- 2: tumor
### HepaticVessel
- 0: background
- 1: vessel
- 2: tumor
### Spleen
- 0: background
- 1: spleen
### Colon
- 0: background
- 1: colon
## Uses
```python
from datasets import load_dataset
miniMSD244 = load_dataset("chehablaborg/miniMSD", split="244")
sample_id = 312
organ = miniMSD244[sample_id]["organ"]
image = miniMSD244[sample_id]["image"]
binary_mask = miniMSD244[sample_id]["binary_mask"]
classes_mask = miniMSD244[sample_id]["classes_mask"]
plt.imshow(image, cmap="grey")
plt.show()
```
## Citation
Please mention us in an acknowledgement [chehablab.com](https://chehablab.com) and cite the original authors of the dataset
```bib
@misc{msd2019,
title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
author={Amber L. Simpson and Michela Antonelli and Spyridon Bakas and Michel Bilello and Keyvan Farahani and Bram van Ginneken and Annette Kopp-Schneider and Bennett A. Landman and Geert Litjens and Bjoern Menze and Olaf Ronneberger and Ronald M. Summers and Patrick Bilic and Patrick F. Christ and Richard K. G. Do and Marc Gollub and Jennifer Golia-Pernicka and Stephan H. Heckers and William R. Jarnagin and Maureen K. McHugo and Sandy Napel and Eugene Vorontsov and Lena Maier-Hein and M. Jorge Cardoso},
year={2019},
eprint={1902.09063},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/1902.09063},
}
```
## License
This work is licensed under a [Creative Commons CC BY SA License](http://creativecommons.org/licenses/by-sa/4.0/).
[](http://creativecommons.org/licenses/by-sa/4.0/)
[Chehab lab](https://chehablab.com) @ 2026 |