Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images

The B-mode ultrasound based computer-aided diagnosis (CAD) has demon-strated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants. However, due to effect of speckle noise in ultrasound im-ages, it is still a challenge task to accurately detect hip landmarks. In this work, we propose a novel hip landmark detection model by integrating the Topological GCN (TGCN) with an Improved Conformer (TGCN-ICF) into a unified framework to improve detection performance. The TGCN-ICF in-cludes two subnetworks: an Improved Conformer (ICF) subnetwork to gen-erate heatmaps and a TGCN subnetwork to additionally refine landmark de-tection. This TGCN can effectively improve detection accuracy with the guidance of class labels. Moreover, a Mutual Modulation Fusion (MMF) module is developed for deeply exchanging and fusing the features extracted from the U-Net and Transformer branches in ICF. The experimental results on the real DDH dataset demonstrate that the proposed TGCN-ICF outper-forms all the compared algorithms.