  Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging
modality that allows non-invasive imaging of medical anatomy without radiation
exposure. Surface reconstruction of US volume is vital to acquire the accurate
anatomical structures needed for modeling, registration, and visualization.
However, traditional methods cannot produce a high-quality surface due to image
noise. Despite improvements in smoothness, continuity, and resolution from deep
learning approaches, research on surface reconstruction in freehand 3D US is
still limited. This study introduces FUNSR, a self-supervised neural implicit
surface reconstruction method to learn signed distance functions (SDFs) from US
volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D
queries sampled around volumetric point clouds to approximate the surface,
guided by two novel geometric constraints: sign consistency constraint and
onsurface constraint with adversarial learning. Our approach has been
thoroughly evaluated across four datasets to demonstrate its adaptability to
various anatomical structures, including a hip phantom dataset, two vascular
datasets and one publicly available prostate dataset. We also show that smooth
and continuous representations greatly enhance the visual appearance of US
data. Furthermore, we highlight the potential of our method to improve
segmentation performance, and its robustness to noise distribution and motion
perturbation.
