README.md

---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- object-detection
task_ids: []
pretty_name: arcade_combined_export
tags:
- fiftyone
- image
- object-detection
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3000 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = load_from_hub("pjramg/arcade_fiftyone")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for arcade_combined_export

<!-- Provide a quick summary of the dataset. -->





This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3000 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("pjramg/arcade_fiftyone")

# Launch the App
session = fo.launch_app(dataset)
```


# ARCADE Combined Dataset (FiftyOne Format)

The **ARCADE Combined Dataset** is a curated collection of coronary angiography images and annotations designed to evaluate coronary artery stenosis. This version has been processed and exported using [FiftyOne](https://voxel51.com/fiftyone), and includes cleaned segmentation data, metadata fields for clinical context, and embedded visual labels.

## Dataset Structure

- `segmentations`: COCO-style detection masks per coronary artery segment.
- `phase`: The acquisition phase of the angiography video.
- `task`: A specific labeling task (segmentation or regression) is used.
- `subset_name`: Subdivision info (train, val, test).
- `coco_id`: Corresponding COCO ID for alignment with original sources.
- `filepath`: Path to the image file.
- `metadata`: Image metadata including dimensions and pixel spacing.

## Format

This dataset is stored in **FiftyOneDataset format**, which consists of:
- `data.json`: Metadata and label references
- `data/`: Folder containing all image samples
- Optional: auxiliary files (e.g., `README.md`, config, JSON index)

To load it in Python:

```python
import fiftyone as fo
dataset = fo.Dataset.from_dir(
    dataset_dir="arcade_combined_fiftyone",
    dataset_type=fo.types.FiftyOneDataset,
)
```
## Source

The original ARCADE dataset was introduced in the paper:

Labrecque Langlais et al. (2023) — Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies.
https://doi.org/10.21203/rs.3.rs-3610879/v1

This combined version aggregates and restructures subsets across tasks and phases, harmonized with FiftyOne tooling for streamlined model training and evaluation.

## License
This dataset is shared for research and academic use only. Please consult the original dataset license for clinical or commercial applications.


## Citation

```bibtex
@article{avram2023evaluation,
  title={Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies},
  author={Labrecque Langlais, E. and Corbin, D. and Tastet, O. and Hayek, A. and Doolub, G. and Mrad, S. and Tardif, J.-C. and Tanguay, J.-F. and Marquis-Gravel, G. and Tison, G. and Kadoury, S. and Le, W. and Gallo, R. and Lesage, F. and Avram, R.},
  year={2023}
}
```

## Dataset Card Contact

[Paula Ramos](https://huggingface.co/datasets/pjramg)