---
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: train
    num_bytes: 2349940926.0
    num_examples: 95311
  download_size: 2310896675
  dataset_size: 2349940926.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- organs
- medical
- ct
- mri
pretty_name: Mini Medical Segmentation Decathlon 244
size_categories:
- 10K<n<100K
---

# 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 serialized 2D RGB images, alongside their corresponding segmentation masks.
The dataset is provided in multiple resolution variants ([244](https://huggingface.co/datasets/chehablaborg/miniMSD244)
and [512](https://huggingface.co/datasets/chehablaborg/miniMSD512)), 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

<!-- Address questions around how the dataset is intended to be used. -->

```python
from datasets import load_dataset
miniMSD244 = load_dataset("chehablaborg/miniMSD244", split="train")

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()
```

## Authors

[Charbel Toumieh](https://www.linkedin.com/in/charbeltoumieh/)

[Ahmad Mustapha](https://www.linkedin.com/in/ahmad-mustapha-ml/)

[Ali Chehab](https://www.linkedin.com/in/ali-chehab-31b05a3/)


## Citation

```
@dataset{minimsd2026,
  title        = {MiniMSD},
  author       = {Toumieh, Charbel and Mustapha, Ahmad and Chehab, Ali},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/chehablaborg/miniMSD244}},
}
```

## Acknowledgment
[Chehab lab](https://chehablab.com) @ 2026