Region Proposal Network with IoU-Balance Loss and Graph Prior for Landmark Detection in 3D Ultrasound

3D ultrasound (US) can improve the prenatal examinations for fetal growth monitoring. Detecting anatomical landmarks of fetus in 3D US has plenty of applications. Classical methods directly regress the coordinates or gaussian heatmaps of landmarks. However, these methods tend to show drawbacks when facing with the large volume and poor image quality of 3D US images. Different from previous methodology, in this work, we propose a successful and first investigation about exploiting object detection framework for landmark detection in 3D US. By regressing multiple parameters of the landmark-centered bounding box (B-box) with strict criteria, object detection framework presents potentials in outperforming previous landmark detection methods. Specifically, we choose the region proposal network (RPN) with localization and classification branches as our backbone for detection efficiency. Based on 3D RPN, we propose to adopt an IoU-balance loss to enhance the communication between two branches and promote the landmark localization. Furthermore, we build a distance based graph prior to regularize the landmark localization and therefore reduce false positives. We validate our method on the challenging task of detection for five fetal facial landmarks. Regarding the landmark localization and classification criteria, our method outperforms the state-of-the-art methods in efficacy and efficiency.