  3D ultrasound (US) can facilitate detailed prenatal examinations for fetal
growth monitoring. To analyze a 3D US volume, it is fundamental to identify
anatomical landmarks of the evaluated organs accurately. Typical deep learning
methods usually regress the coordinates directly or involve heatmap-matching.
However, these methods struggle to deal with volumes with large sizes and the
highly-varying positions and orientations of fetuses. In this work, we exploit
an object detection framework to detect landmarks in 3D fetal facial US
volumes. By regressing multiple parameters of the landmark-centered bounding
box (B-box) with a strict criteria, the proposed model is able to pinpoint the
exact location of the targeted landmarks. Specifically, the model uses a 3D
region proposal network (RPN) to generate 3D candidate regions, followed by
several 3D classification branches to select the best candidate. It also adopts
an IoU-balance loss to improve communications between branches that benefits
the learning process. Furthermore, it leverages a distance-based graph prior to
regularize the training and helps to reduce false positive predictions. The
performance of the proposed framework is evaluated on a 3D US dataset to detect
five key fetal facial landmarks. Results showed the proposed method outperforms
some of the state-of-the-art methods in efficacy and efficiency.
