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--- |
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license: cc-by-nc-sa-2.0 |
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tags: |
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- medical |
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size_categories: |
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- 10K<n<100K |
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--- |
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# PAX-Ray++ Dataset |
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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. |
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## Key Features |
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- **Large Dataset**: Contains **7,377 frontal and lateral view images**, each carefully pseudo-labeled. |
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- **Fine-Grained Annotation**: Offers annotations for **157 distinct anatomical classes**, ensuring comprehensive coverage of thoracic anatomy. |
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- **Extensive Instances**: Includes **over 2 million annotated instances**, providing a robust foundation for training and evaluation. |
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- **2D Projection of 3D Data**: Combines the richness of 3D CT data with the accessibility of 2D radiographic images. |
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## Applications |
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The PAX-Ray++ dataset is designed to support: |
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- Anatomical segmentation in chest X-rays. |
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- Development of machine learning models for medical imaging tasks. |
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- Research on transfer learning between CT-derived and true radiographic images. |
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## Related Repositories |
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### 1. **Dataset Dataloaders** |
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[2D Anatomy Datasets](https://github.com/ConstantinSeibold/2DAnatomyDatasets) |
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This repository provides dataloaders for PAX-Ray++ and other datasets, making it easy to integrate the dataset into your machine learning pipelines. |
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### 2. **Model Development and Applications** |
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[Chest X-Ray Anatomy Segmentation](https://github.com/ConstantinSeibold/ChestXRayAnatomySegmentation) |
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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++. |
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## Getting Started |
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### Download the Dataset |
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```bash |
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git lfs install |
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git clone [email protected]:datasets/cmseibold/PAX-RayPlusPlus |
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``` |
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or follow the instructions in [2D Anatomy Datasets](https://github.com/ConstantinSeibold/2DAnatomyDatasets/blob/main/src/prepare_data/prepare_paxraypp/get_paxraypp_full.sh) : |
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``` |
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sh src/prepare_data/prepare_paxraypp/get_paxraypp_full.sh |
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``` |
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### Dataset Structure |
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The dataset consists of: |
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- **Frontal View Images**: Annotated radiographs projected from thorax CT scans. |
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- **Lateral View Images**: Corresponding annotations for lateral projections. |
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Annotations are provided in a compatible COCO format for easy integration with common machine learning frameworks such as MMDetection and Detectron2. |
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### Requirements |
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Make sure to install the required libraries by following the instructions in the [2D Anatomy Datasets](https://github.com/ConstantinSeibold/2DAnatomyDatasets) repository. |
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## Citation |
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If you use the PAX-Ray++ dataset in your research or projects, please consider citing the dataset. |
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```latex |
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@inproceedings{Seibold_2023_CXAS, |
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author = {Constantin Seibold, Alexander Jaus, Matthias Fink, |
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Moon Kim, Simon Reiß, Jens Kleesiek*, Rainer Stiefelhagen*}, |
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title = {Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling}, |
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year = {2023}, |
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} |
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``` |
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## Feedback and Contributions |
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Contributions, feedback, or questions are welcome! Feel free to open an issue or submit a pull request in the relevant repositories. |
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