bibtext: @InProceedings{ Hua_TopologicalGCN_MICCAI2024,
                   author = { Huang, Tianxiang and Shi, Jing and Jin, Ge and Li, Juncheng and Wang, Jun and Du, Jun and Shi, Jun },
                   title = { { Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: Topological GCN for Improving Detection of Hip Landmarks from B-Mode Ultrasound Images
authors: Huang, Tianxiang Shi, Jing Jin, Ge Li, Juncheng Wang, Jun Du, Jun Shi, Jun