Deep-learning soft-tissue decomposition in chest radiography using fast fuzzy C-means clustering with CT datasets

Abstract

Chest radiography is the most routinely used X-ray imaging technique for screening and diagnosing lung and chest disease, such as lung cancer and pneumonia. However, the clinical interpretation of the hidden and obscured anatomy in chest X-ray images remains challenging because of the bony structures overlapping the lung area. Thus, multi-perspective imaging with a high radiation dose is often required. In this study, to address this problem, we propose a deep-learning soft-tissue decomposition method using fast fuzzy C-means (FFCM) clustering with computed tomography (CT) datasets. In this method, FFCM clustering is used to decompose a CT image into bone and soft-tissue components, which are synthesized into digitally reconstructed radiographs (DRRs) to obtain large amounts of X-ray decomposition datasets as ground truths for training. In the training stage, chest and soft-tissue DRRs are used as input and label data, respectively, for training the network. During the testing, a chest X-ray image is fed into the trained network to output the corresponding soft-tissue image component. To verify the efficacy of the proposed method, we conducted a feasibility study on clinical CT datasets available from the AAPM Lung CT Challenge. According to our results, the proposed method effectively yielded soft-tissue decomposition from chest X-ray images; this is encouraging for reducing the visual complexity of chest X-ray images. Consequently, the finding of our feasibility study indicate that the proposed method can offer a promising outcome for this purpose.