Automatic delineation of ribs and clavicles in chest radiographs using fully convolutional DenseNets. (October 2019)

• Record Type: Journal Article
• Authors: Liu, Yunbi; Zhang, Xiao; Cai, Guangwei; Chen, Yingyin; Yun, Zhaoqiang; Feng, Qianjin; Yang, Wei
• Is Part Of: Computer methods and programs in biomedicine. Volume 180(2019)
• Publication Date: 2019-10
• Subjects: Chest radiograph -- Rib and clavicle delineation -- Fully convolutional DenseNet; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105014
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Our proposed method is one of the few deep learning based methods to delineate ribs and clavicles in chest radiographs. Our proposed method is one of the few methods that can delineate the anterior ribs in chest radiographs. A pixel-weighted loss function is designed to ignore the uncertain pixels for robust delineation. A preliminary result of suppressing the bone components in chest radiographs has been produced by using our delineating system. Abstract: Background and Objective: In chest radiographs (CXRs), all bones and soft tissues are overlapping with each other, which raises issues for radiologists to read and interpret CXRs. Delineating the ribs and clavicles is helpful for suppressing them from chest radiographs so that their effects can be reduced for chest radiography analysis. However, delineating ribs and clavicles automatically is difficult by methods without deep learning models. Moreover, few of methods without deep learning models can delineate the anterior ribs effectively due to their faint rib edges in the posterior-anterior (PA) CXRs. Methods: In this work, we present an effective deep learning method for delineating posterior ribs, anterior ribs and clavicles automatically using a fully convolutional DenseNet (FC-DenseNet) as pixel classifier. We consider a pixel-weighted loss function to mitigate the uncertainty issue during manually delineating for robust prediction. Results: We conduct a comparative analysis with two other fully convolutional networks for edge detection and the state-of-the-art method without deep learning models. The proposed method significantly outperforms these methods in terms of quantitative evaluation metrics and visual perception. The average recall, precision and F-measure are 0.773 ± 0.030, 0.861 ± 0.043 and 0.814 ± 0.023 respectively, and the mean boundary distance (MBD) is 0.855 ± 0.642 pixels of the proposed method on the test dataset. The proposed method also performs well on JSRT and NIH Chest X-ray datasets, indicating its generalizability across multiple databases. Besides, a preliminary result of suppressing the bone components of CXRs has been produced by using our delineating system. Conclusions: The proposed method can automatically delineate ribs and clavicles in CXRs and produce accurate edge maps.