Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images
This work addresses a domain-specific problem for medical imaging in infant hip diagnosis, offering an incremental improvement by refining landmark detection with a novel integration of methods.
The authors tackled the challenge of accurately detecting hip landmarks in B-mode ultrasound images for diagnosing Developmental Dysplasia of the Hip in infants, proposing a TGCN-ICF model that integrates a Topological GCN with an Improved Conformer, and it outperformed all compared algorithms on a real DDH dataset.
The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated 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 frame-work to improve detection performance. The TGCN-ICF includes two subnet-works: an Improved Conformer (ICF) subnetwork to generate heatmaps and a TGCN subnetwork to additionally refine landmark detection. This TGCN can effectively improve detection accuracy with the guidance of class labels. Moreo-ver, a Mutual Modulation Fusion (MMF) module is developed for deeply ex-changing 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 outperforms all the compared algorithms.