Multi-view Adaptive Bone Activation from Chest X-Ray with Conditional Adversarial Nets

Activating bone from a chest X-ray (CXR) is significant for disease diagnosis and health equity for under-developed areas, while the complex overlap of anatomical structures in CXR constantly challenges bone activation performance and adaptability. Besides, due to high data collection and annotation costs, no large-scale labeled datasets are available. As a result, existing methods commonly use single-view CXR with annotations to activate bone. To address these challenges, in this paper, we propose an adaptive bone activation framework. This framework leverages the Dual-Energy Subtraction (DES) images to consist of multi-view image pairs of the CXR and the contrastive learning theory to construct training samples. In particular, we first devise a Siamese/Triplet architecture supervisor; correspondingly, we establish a cGAN-styled activator based on the learned skeletal information to generate the bone image from the CXR. To our knowledge, the proposed method is the first multi-view bone activation framework obtained without manual annotation and has more robust adaptability. The mean of Relative Mean Absolute Error ((RMAE) over bar) and the Frechet Inception Distance (FID) are 3.45% and 1.12 respectively, which proves the results activated by our method retain more skeletal details with few feature distribution changes. From the visualized results, our method can activate bone images from a single CXR ignoring overlapping areas. Bone activation has drastically improved compared to the original images.