A Deep Fully Convolutional Network for Distal Radius and Ulna Semantic Segmentation. (October 2019)

• Record Type: Journal Article
• Authors: Wang, Shuqiang; Liang, Wei; Wang, Hongfei; Chen, Zhuo; Lu, Yiqian
• Is Part Of: IOP conference series. Volume 646(2019)
• Publication Date: 2019-10
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/646/1/012025
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Semantic segmentation is an essential step to do further image analysis and scene understanding tasks. In medical imaging analysis applications, it is even more challenging to do automatic segmentation due to tissues' complicated boundaries. In this paper, a fully convolutional network (FCN) based model is constructed to segment distal radius and ulna (DRU) areas from hand X-ray images. We evaluated the proposed network on a clinical DRU dataset with different network configurations. The proposed network can achieve 98% accuracy and 96% mean Intersection over Union (IoU).