Datasets:
File size: 4,857 Bytes
5c988bc b6a68b5 7aded6a b6a68b5 7aded6a b6a68b5 1e35d4a b6a68b5 1e35d4a b6a68b5 1e35d4a b6a68b5 5c988bc 1e35d4a a17af45 1e35d4a f0f5a83 1e35d4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | ---
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 |