CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules. (May 2022)

• Record Type: Journal Article
• Authors: Gao, Yuan; Liu, Hongzhi; Jiang, Liang; Yang, Chunfeng; Yin, Xindao; Coatrieux, Jean-Louis; Chen, Yang
• Is Part Of: Biomedical signal processing and control. Volume 75(2022)
• Publication Date: 2022-05
• Subjects: Rib fracture -- Deep learning -- Contralateral module -- Contextual module -- Edge enhanced module; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103620
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Rib fracture is a common disease that requires prompt treatment. This study focuses on developing a rib fracture diagnosis deep learning method using contralateral, contextual, and edge enhanced modules and evaluating its detection performance. A novel rib fracture diagnosis method was designed, named CCE-Net. To evaluate the performance of this method, 1639 digital radiography (DR) images were enrolled. Fracture features were extracted for three modules: contralateral, contextual, and edge enhanced modules. These modules can be used to identify fracture features in rib DR images, imitating the experience of broad-certificated radiologists. The contralateral module assists in diagnosing rib fractures by comparing the difference between the detected target region and the contralateral region. The contextual module helps to aid rib fracture detection by extracting contextual features. The edge enhanced module improves the accuracy of fracture detection by enhancing the edge information of the rib bone. The head of this two-stage detection network uses the multi-path fusion mechanism as the main architecture to integrate and utilize the above modules. The qualitative results show that with the ground truth of rib fracture as the evaluation standard, CCE-Net can achieve a better visual effect of fracture detection than other methods. The quantitative results show that CCE-Net can achieve the best performance in various detection indicators include AP50 0.911, AP75 0.794, AP25 0.913, and Recall 0.934. Experimental results show that CCE-Net can acquire the excellent ability of rib fracture diagnosis. We invasion that this approach will be applied to clinical study.