Segmentation of x-ray images using deep learning trained on synthetic data

Abstract: Radiograph examinations play a critical role in various applications such as the detection of bone pathologies and lung cancer, despite the challenge of false negatives. The integration of Artificial Intelligence (AI) holds promise in enhancing image quality and assisting radiologists in their diagnostic processes. However, the scarcity of annotated high-quality data poses a significant hurdle in training AI models effectively. In this thesis, we propose a method for training deep learning models using synthetic data to achieve segmentation of X-ray images. Realistic, simulated, images were generated, enabling segmentation of anatomical structures, including the spine, ribs, scapula, clavicle, and lungs, on a test set comprised of other simulated images. The foremost emphasize was placed on the segmentation of the spine, where we obtained a Dice score of 0.87. Significant advancements have also been made in the application of the model to real clinical images, demonstrating successful segmentation in certain instances. Further generalization of the model opens up numerous avenues for future exploration of deep learning in radiography.