Multi-Task Medical Image-to-Images Translation using Transformer for Chest X-Ray Radiography

Abstract

Chest X-ray is one of the main methods for screening chest diseases, which has the characteristics of low radiation dose, fast imaging and low cost. In order to better assist doctors in disease diagnosis, usually the X-ray bone suppression and organ segmentation are performed. Many research progress has been made in this field, but the accuracy of the above two tasks is still limited due to the inherent characteristics of medical images. Firstly, the shape of organs of different individuals varies greatly.So there are inevitable segmentation errors if the overall shape is not perceived. Generally, the boundary of organs is fuzzy, so it is prone to misclassification near the boundary. In addition, existing bone suppression methods still can't completely remove bone shadows. In this paper, we propose a deep learning model whose overall architecture is designed based on the pix2pix network. This model generates both bone suppression images and organ segmentation images.Aiming at the above three issues, we make some improvements. We innovatively use the Transformer structure to enhance the attention to the global context and enhance the perception of the overall shape of the organ in the feature extraction process. Also, we design a new loss function, which gives larger weight to the position error near the organ boundary in the later stage of network training. This loss function pays more attention to edge information and helps determine the position of organ boundaries. We evaluate the effectiveness of the model on the X-ray image dataset, and compare it with the latest algorithm comprehensively. We also evaluate the effectiveness of our improvements through ablation study which shows that our improvements are effective.