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https://github.com/neuro-ml/amid
Awesome Medical Imaging Datasets
https://github.com/neuro-ml/amid
datasets medical-imaging python
Last synced: 5 days ago
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Awesome Medical Imaging Datasets
- Host: GitHub
- URL: https://github.com/neuro-ml/amid
- Owner: neuro-ml
- License: mit
- Created: 2022-05-27T19:24:03.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-10-04T16:29:48.000Z (4 months ago)
- Last Synced: 2025-01-22T18:08:31.099Z (12 days ago)
- Topics: datasets, medical-imaging, python
- Language: Python
- Homepage: https://neuro-ml.github.io/amid/
- Size: 2.57 MB
- Stars: 40
- Watchers: 5
- Forks: 8
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://neuro-ml.github.io/amid/)
[](https://neuro-ml.github.io/amid/latest/CONTRIBUTING/)
[](https://pypi.org/project/amid/)
Awesome Medical Imaging Datasets (AMID) - a curated list of medical imaging datasets with unified interfaces
# Getting started
Just import a dataset and start using it!
Note that for some datasets you must manually download the raw files first.
```python
from amid.verse import VerSe
ds = VerSe(root='/path/to/raw/data')
# get the available ids
print(len(ds.ids))
i = ds.ids[0]
# use the available methods:
# load the image and vertebrae masks
x, y = ds.image(i), ds.masks(i)
print(ds.split(i), ds.patient(i))
# or get a namedTuple-like object:
entry = ds(i)
x, y = entry.image, entry.masks
print(entry.split, entry.patient)
```
# Available datasets
| Name | Entries | Body region | Modality |
|:-----------------------------------------------------------------------------------------------------------------------------------|----------:|:------------------------------------|:-----------------------------------------------------------------------|
| AMOS | 2465 | Abdomen | CT, MRI |
| BIMCVCovid19 | 16364 | Chest | CT |
| BraTS2021 | 5880 | Head | MRI T1, MRI T1Gd, MRI T2, MRI T2-FLAIR |
| CC359 | 359 | Head | MRI T1 |
| CLDetection2023 | 400 | Head | X-ray |
| CRLM | 197 | Abdomen | CT, SEG |
| CT_ICH | 75 | Head | CT |
| CURVAS | 90 | Abdomen | CT |
| CrossMoDA | 484 | Head | MRI T1c, MRI T2hr |
| DeepLesion | 20094 | Abdomen, Thorax | CT |
| EGD | 3096 | Head | FLAIR, MRI T1, MRI T1GD, MRI T2 |
| FLARE2022 | 2100 | Abdomen | CT |
| HCP | 1113 | Head | MRI |
| KiTS23 | 489 | thorax | CT |
| LIDC | 1018 | Chest | CT |
| LiTS | 201 | Abdominal | CT |
| LiverMedseg | 50 | Chest, Abdomen | CT |
| MIDRC | 229 | Thorax | CT |
| MOOD | 1358 | Head, Abdominal | MRI, CT |
| MSD | 2628 | Chest, Abdominal, Head | CT, CE CT, MRI, MRI FLAIR, MRI T1w, MRI t1gd, MRI T2w, MRI T2, MRI ADC |
| MSLUB | 70 | Head | MRI |
| Medseg9 | 9 | Chest | CT |
| MoscowCancer500 | 979 | Thorax | CT |
| MoscowCovid1110 | 1110 | Thorax | CT |
| NLST | 26254 | Thorax | CT |
| NSCLC | 422 | Thorax | CT |
| RSNABreastCancer | 54710 | Thorax | MG |
| RibFrac | 660 | Chest | CT |
| StanfordCoCa | 1000 | Coronary, Chest | CT |
| TBAD | 100 | Chest | CT |
| Totalsegmentator | 1204 | Head, Thorax, Abdomen, Pelvis, Legs | CT |
| UPENN_GBM | 671 | Head | FLAIR, MRI T1, MRI T1GD, MRI T2, DSC MRI, DTI MRI |
| VSSEG | 484 | Head | MRI T1c, MRI T2 |
| VerSe | 374 | Thorax, Abdomen | CT |
Check out [our docs](https://neuro-ml.github.io/amid/) for a more detailed list of available datasets and their fields.
# Install
Just get it from PyPi:
```shell
pip install amid
```
# Contribute
Check our [contribution guide](https://neuro-ml.github.io/amid/latest/CONTRIBUTING/) if you want to add a new dataset to
AMID.