MDA-GAN: Multiscale and Dual Attention Generative Adversarial Network for Bone Suppression in Chest X-Rays

Abstract

The bone structure in a chest x-ray creates trouble for a radiologist to examine the organs, manifestation of disease, and hidden tiny abnormalities. Bone suppression in chest x-rays allows better examination of lung fields. This has the potential to improve diagnostic accuracy. Dual-energy subtraction imaging is a standard bone suppression technique that delivers a higher dose of radiation and requires specific hardware. This article proposes a novel multiscale and dual attention-guided generative adversarial network (MDA-GAN) to transform chest x-rays into bone-suppressed x-rays in an unsupervised manner. We incorporate a spatial attention module to generate attention maps that were further concatenated with the coarsely generated bone segmentation mask. This dual attention is introduced to the generator at multiple scales in between the skip connection of the encoder and decoder layer. The proposed dual attention multiscale mechanism helps the generator to learn that only bones need to be removed on the chest x-ray without tempering the remaining parts. The proposed MDA-GAN is trained with adversarial loss combined with deep supervised cycle consistency and structure similarity for unpaired training images. We employ supervision heads in all the decoder layers to convert the activation maps into an output comparable to the scaled-down images and minimize the cycle consistency loss in a deep supervised manner. Experiments are conducted on an unpaired dataset including the public and our in-house Indian dataset and results show that incorporating dual attention at multiple scales and deep cycle consistency in translation networks significantly improves the quality of bone-suppressed images. (https://github.com/rB080/ribsup.git.)