In this thesis, we study methods to reduce the amount of data needed to create deep learning models that can detect defects in bones from X-ray images. Detecting defects in bones from X-ray images and properly annotating the images is the paramount step when it comes to corrective surgeries of bones. Annotations or labels, such as radial inclination and volar tilt are measurements that are necessary for many corrective surgeries. Generating these annotations is an arduous and manual task for medical professionals. By being able to automate the process of generating these annotations, it will be possible to reduce a significant amount of labor of these professionals.
Modern deep learning models are heavily reliant upon availability of a large amount of properly labeled data for their training. In this thesis, we experimented to find methods to create appropriate synthetic data that can be combined with natural data to train deep learning models. We designed three deep learning models to generate two different forms of annotations. The first goal was to use cycle consistent generative adversarial networks to create proper synthetic images. Then we used the synthetic images to improve classifier models that can detect defects in bones. In the end, we expanded the cycle consistent generative adversarial network so that it can accommodate three input domains instead of two and called it multi-cycleGAN. We used multi-cycleGAN to segment bones from natural X-ray images.
Our experiments concluded that by adding proper synthetic images with natural images, we can improve the performance of classifiers significantly and circumvent the persistent issue of unavailability of data. However, the multi-cycleGAN model did not generate a very accurate segmentation of bones. It was able to segment bones of forearm better than bones of wrist. It was able to understand the overall shape and positioning of the wrists in X-ray images but it did not produce proper segmentations of the individual fingers
