---
license: cc-by-nc-sa-2.0
tags:
- medical
size_categories:
- 10K<n<100K
---

# PAX-Ray++ Dataset

The **PAX-Ray++ Dataset** is a high-quality dataset designed to facilitate segmentation tasks for anatomical structures in chest radiographs. By leveraging pseudo-labeled thorax CT scans projected onto a 2D plane, this dataset provides fine-grained annotations resembling traditional X-ray imaging. This enables the development and evaluation of models tailored to anatomical segmentation in medical imaging.

---

## Key Features

- **Large Dataset**: Contains **7,377 frontal and lateral view images**, each carefully pseudo-labeled.
- **Fine-Grained Annotation**: Offers annotations for **157 distinct anatomical classes**, ensuring comprehensive coverage of thoracic anatomy.
- **Extensive Instances**: Includes **over 2 million annotated instances**, providing a robust foundation for training and evaluation.
- **2D Projection of 3D Data**: Combines the richness of 3D CT data with the accessibility of 2D radiographic images.

---

## Applications

The PAX-Ray++ dataset is designed to support:
- Anatomical segmentation in chest X-rays.
- Development of machine learning models for medical imaging tasks.
- Research on transfer learning between CT-derived and true radiographic images.

---

## Related Repositories

### 1. **Dataset Dataloaders**
[2D Anatomy Datasets](https://github.com/ConstantinSeibold/2DAnatomyDatasets)  
This repository provides dataloaders for PAX-Ray++ and other datasets, making it easy to integrate the dataset into your machine learning pipelines.

### 2. **Model Development and Applications**
[Chest X-Ray Anatomy Segmentation](https://github.com/ConstantinSeibold/ChestXRayAnatomySegmentation)  
Explore pre-trained models and pipelines designed specifically for the PAX-Ray++ dataset and other similar datasets. This repository demonstrates how to apply segmentation models trained on PAX-Ray++.

---

## Getting Started

### Download the Dataset

```bash
git lfs install
git clone git@hf.co:datasets/cmseibold/PAX-RayPlusPlus
```

or follow the instructions in [2D Anatomy Datasets](https://github.com/ConstantinSeibold/2DAnatomyDatasets/blob/main/src/prepare_data/prepare_paxraypp/get_paxraypp_full.sh) :

```
sh src/prepare_data/prepare_paxraypp/get_paxraypp_full.sh
```

### Dataset Structure
The dataset consists of:
- **Frontal View Images**: Annotated radiographs projected from thorax CT scans.
- **Lateral View Images**: Corresponding annotations for lateral projections.  

Annotations are provided in a compatible COCO format for easy integration with common machine learning frameworks such as MMDetection and Detectron2.

### Requirements
Make sure to install the required libraries by following the instructions in the [2D Anatomy Datasets](https://github.com/ConstantinSeibold/2DAnatomyDatasets) repository.

---

## Citation
If you use the PAX-Ray++ dataset in your research or projects, please consider citing the dataset.

```latex
@inproceedings{Seibold_2023_CXAS,
author    = {Constantin Seibold, Alexander Jaus, Matthias Fink,
Moon Kim, Simon Reiß, Jens Kleesiek*, Rainer Stiefelhagen*},
title     = {Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling},
year      = {2023},
}
```

---

## Feedback and Contributions
Contributions, feedback, or questions are welcome! Feel free to open an issue or submit a pull request in the relevant repositories.