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 14,753 projections from 7,377 CT volumes with frontal and lateral view images, each carefully pseudo-labeled.
• Fine-Grained Annotation: Offers annotations for 159 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
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
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++.