---
viewer: false
license: cc-by-nc-4.0
task_categories:
- image-feature-extraction
language:
- en
tags:
- medical
- image
- biometry
- biometrics
- measurement
- mri
- brain
pretty_name: feta24-biometrics
size_categories:
- n<1K
---


## About
**FeTA24-Biometrics** is based on [FeTA 2024 (FeTA24) data from the University Children’s Hospital Zurich](https://fetachallenge.github.io/pages/Data_download).

**Dataset summary:**
- 65 T2-weighted brain MR scans, 7-class segmentation masks, and 5 biometric measurements for each case

**What's new?** 
- We transformed the original images and segmentaion masks into the biometric measurement space
  using the transformation matrix in FeTA24 dataset.
- We converted the landmark masks into JSON files.
  - Step 1: Get the corrdinates of 2 landmarks for each measurement.
  - Step 2: Label landmarks according to the definitions in `landmarks_map`.
  - Step 3: Save the coordinates of all 10 landmarks to a JSON file.
- We provided visualization of biometric measurements in corresponding 2D slices and landmarks.
- Cases without biometric measurement data are excluded from FeTA24.


## Changelog 🔥
- [2 Mar, 2025] Update data structure in JSON files within `Landmarks.zip`. Landmarks coordinates remain unchanged.
- [2 Mar, 2025] Initial release 


## Data Usage Agreement
By using the dataset, you agree to the terms as follow.
- You must agree to the [terms of use for FeTA24 dataset](https://www.synapse.org/Synapse:syn25649159/wiki/610007)
- You must refer to the source of this dataset in any publication:
  `https://huggingface.co/datasets/YongchengYAO/FeTA24-Biometrics`


## Download from Huggingface
```bash
#!/bin/bash
pip install --upgrade huggingface-hub[cli]
huggingface-cli login --token $HF_TOKEN
```
```python
# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/FeTA24-Biometrics", repo_type='dataset', local_dir="/your/local/folder")
```


## Data Info
### Landmarks:
```python
landmarks_map = {
    "P1": "most anterior point of corpus callosum",
    "P2": "most posterior point of corpus callosum",
    "P3": "most superior point of vermis",
    "P4": "most inferior point of vermis",
    "P5": "right parietal eminence ",
    "P6": "left parietal eminence",
    "P7": "right skull parietal eminence",
    "P8": "left skull parietal eminence",
    "P9": "most right point of cerebellar hemisphere",
    "P10": "most left point of cerebellar hemisphere",
}
```

### Biometric measurement:
```python
biometrics_map = {
    "1": "Corpus_Callosum_Length",
    "2": "Vermis_Height",
    "3": "Brain_Biparietal_Diameter",
    "4": "Skull_Biparietal_Diameter",
    "5": "Transverse_Cerebellar_Diameter",
}
```

### Slice dimensions for measurements/landmarks:
-  {"P1": 0} means the landmark point P1 should be located from dim 0 (i.e., sagittal slices)
-  0: sagittal slice
-  1: coronal slice
-  2: axial slice
```python
LANDMARKS_SLICE_DIM = {
    "P1": 0,
    "P2": 0,
    "P3": 0,
    "P4": 0,
    "P5": 2,
    "P6": 2,
    "P7": 2,
    "P8": 2,
    "P9": 1,
    "P10": 1,
}
```


## License
This dataset is released under the `CC BY-NC 4.0` license.