Update README.md

README.md

@@ -4,4 +4,83 @@ tags:
 - medical
 size_categories:
 - 10K<n<100K
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+---
+
+# 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 [email protected]: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.
+
+
+@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.