Agent Based Fetal Face Segmentation for Standard Plane Localization in 3D Ultrasound

In practice, fetal 3D ultrasound can have difficulty in accurately detecting labels for auxiliary standard cut plane localization because of mass loss. Therefore, in this paper, we propose a new segmentation-based reinforcement learning framework for automatically localizing the standard plane of the face: in 3D fetal ultrasound, the initial plane is localized based on anatomical landmarks of mass and geometric relationships, agents navigate through visual segmentation to automatically localize the standard plane, and bound ultrasound views are presented to show the resultant plane. This study was extensively validated on an in-house large dataset. The accuracy of this automatic localization of 3D ultrasound standard planes with sonographer-calibrated median sagittal views of the face, horizontal transverse views of both eyeballs, and coronal views of the nasolabial was 6.64 °/5.65mm, 7.04°/3.58mm, and 5.14 ° 14.26mm, respectively, with success rates of 66.67 %, 78.38 %, and 80.41 %, respectively. The experimental results verify that this system can effectively improve navigation performance.