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Datasets:
YongchengYAO
/
AFIDs-Lite

Tasks:
Image Feature Extraction
Modalities:
Image
Languages:
English
Size:
< 1K
Tags:
medical
image
mri
brain
landmarks
biometry
Libraries:
Datasets
License:
Dataset card Data Studio Files
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AFIDs-Lite
File size: 4,563 Bytes
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---
license: cc-by-4.0
task_categories:
- image-feature-extraction
language:
- en
tags:
- medical
- image
- mri
- brain
- landmarks
- biometry
pretty_name: 'afids-lite'
size_categories:
- n<1K
---


## About
This is a preprocessed redistribution of [AFIDs](https://github.com/afids/afids-data) (OpenNeuro `ds004470` + `ds004471`), which is released under the `CC BY 4.0` license.

**Dataset summary:** 72 T1-weighted brain MR scans with 32 anatomical fiducial (AFID) landmarks each.

**Contents of this repository:**

- `Images/` — 72 files

📝 Landmark annotations, visualization figures and the benchmark plan files live in 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can load the complete images and annotations from dataset configs.


## Relation to the source dataset

| | |
| --- | --- |
| In the source | 72 T1-weighted MR scans (32 SNSX + 40 LHSCPD), 32 anatomical fiducials each |
| Excluded here | nothing — this is a complete mirror |
| **In this repo** | **72 `Images`** |

**Why `-Lite`?** The suffix marks this as a *derived* redistribution rather than a copy of the source. These are **preprocessed** volumes — every case has been format-converted, geometry-normalised and reoriented to RAS+ — and for some sources cases or modalities are excluded as well (see the table above). Use it to reproduce MedVision, not as a substitute for the original release. See [Preprocessing](#preprocessing) below for exactly what was changed.


## Preprocessing

- Images converted to 3D volumes in `nii.gz` format and standardized to RAS+ orientation.

- The 32 ground-truth fiducials per case were parsed from the `.fcsv` files (`# CoordinateSystem = 0`, i.e. RAS world-mm) and converted to 0-based voxel indices in the RAS+ volume, then saved as JSON.

- One T1w volume is kept per subject (32 SNSX + 40 LHSCPD = 72 cases).


## Landmarks

```python
landmarks_map = {
    "P1": "anterior commissure",
    "P2": "posterior commissure",
    "P3": "infracollicular sulcus",
    "P4": "pontomesencephalic junction",
    "P5": "superior interpeduncular fossa",
    "P6": "right superior lateral mesencephalic sulcus",
    "P7": "left superior lateral mesencephalic sulcus",
    "P8": "right inferior lateral mesencephalic sulcus",
    "P9": "left inferior lateral mesencephalic sulcus",
    "P10": "culmen",
    "P11": "intermammillary sulcus",
    "P12": "right mammillary body",
    "P13": "left mammillary body",
    "P14": "pineal gland",
    "P15": "right lateral ventricle at anterior commissure",
    "P16": "left lateral ventricle at anterior commissure",
    "P17": "right lateral ventricle at posterior commissure",
    "P18": "left lateral ventricle at posterior commissure",
    "P19": "genu of the corpus callosum",
    "P20": "splenium of the corpus callosum",
    "P21": "right anterolateral temporal horn",
    "P22": "left anterolateral temporal horn",
    "P23": "right superior anteromedial temporal horn",
    "P24": "left superior anteromedial temporal horn",
    "P25": "right inferior anteromedial temporal horn",
    "P26": "left inferior anteromedial temporal horn",
    "P27": "right indusium griseum origin",
    "P28": "left indusium griseum origin",
    "P29": "right ventral occipital horn",
    "P30": "left ventral occipital horn",
    "P31": "right olfactory sulcal fundus",
    "P32": "left olfactory sulcal fundus"
}
```


## News
- [25 Jul, 2026] Initial release. This dataset is integrated into 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can use these config names to load data in python:

  - `AFIDs_BiometricsFromLandmarks_Task01_Axial_Test`
  - `AFIDs_BiometricsFromLandmarks_Task01_Axial_Train`
  - `AFIDs_BiometricsFromLandmarks_Task01_Sagittal_Test`
  - `AFIDs_BiometricsFromLandmarks_Task01_Sagittal_Train`


## Data Usage Agreement
By using the dataset, you agree to the terms as follow.
- You must comply with the original `CC BY 4.0` license terms of the source dataset.
- You are recommended to refer to the source of this dataset in any publication: `https://huggingface.co/datasets/YongchengYAO/AFIDs-Lite`
- You must cite the original publication(s):
  - https://doi.org/10.1038/s41597-024-04259-z


## Official Release
For more information, please go to the official site: https://github.com/afids/afids-data


## Download from Huggingface
```python
# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/AFIDs-Lite", repo_type='dataset', local_dir="/your/local/folder")
```