Deep Feature Disentanglement Learning for Bone Suppression in Chest Radiographs

Suppression of bony structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper, we propose a Disentanglement AutoEncoder (DAE) for bone suppression. As the projection of 3D structures of bones and soft tissues overlap in 2D radiographs, their features are interwoven and need to be disentangled for effective bone suppression. Our DAE progressively separates the features of soft-tissues from that of the bony structure during the encoder phase and reconstructs the soft-tissue image based on the disentangled features of soft-tissue. Bone segmentation can be performed concurrently using the separated bony features through a separate multi-task branch. By training the model with multi-task supervision, we explicitly encourage the autoencoder to pay more attention to the locations of bones in order to avoid loss of soft-tissue information. The proposed method is shown to be effective in suppressing bone structures from chest radiographs with very little visual artifacts.