CAMS-Net: An attention-guided feature selection network for rib segmentation in chest X-rays. (April 2023)

• Record Type: Journal Article
• Authors: Zhang, Dandan; Wang, Hongyu; Deng, Jiahui; Wang, Tonghui; Shen, Cong; Feng, Jun
• Is Part Of: Computers in biology and medicine. Volume 156(2023)
• Publication Date: 2023-04
• Subjects: Chest X-ray -- Clavicles and ribs segmentation -- Skip connection -- Attention -- Multi scale; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106702
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Segmentation of clavicles and ribs in chest X-rays is significant for diagnosing lung diseases. However, it is challenging to segment ribs because of the low contrast on chest X-rays. Moreover, most existing methods fail to segment ribs in the area of abnormal gray value caused by overlapping anatomical structures or lesions. A novel algorithm based on attention-guided feature selection named CAMS-Net is presented in this paper, aiming to improve the accuracy of ribs segmentation in low contrast areas and abnormal gray value areas. The collaborative attention skip connection module (CAS) introduces the decoder features and attention-guided feature selection into the traditional skip connection, which highlights the feature representation of ribs. The attention-guided multi-scale feature selection module (AMFS) increases receptive field size to connect the abnormal rib gray value region with the normal gray value region. To reduce the influence of background pixels, the AMFS selects important features through attention and uses multi-scale information to jointly decide the final class of the pixel. The paper conducted extensive experiments to evaluate CAMS-Net. Compared with state-of-the-art methods, the average values of Precision and Jaccard are increased by 0.89% and 1.23%, respectively. The Recall and Jaccard values of the anterior rib are increased by 2.64% and 2.52%, respectively. Qualitative analysis shows that CAMS-Net can improve the segmentation accuracy of low-contrast areas and maintain the segmentation integrity in areas with abnormal gray values. The robustness and generalization of CAMS-Net are verified through external tests on VinDr-RibCXR, JSRT, Shenzhen, and NIH datasets. Besides, CAS and AMFS modules can be flexibly inserted into other backbone networks. Highlights: Segmentation of the chest X-ray plays a critical role in the computer-aided system. CAMS-Net gains high accuracy, e.g. in areas with low contrast or covered by lesions. CAMS-Net shows better performance on five CXR datasets than other methods. CAS enhances the representation of target features in skip connection. AMFS improves the segmentation integrity of ribs in grey value abnormal regions.