Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints
arxiv(2024)
摘要
Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging
modality that allows non-invasive imaging of medical anatomy without radiation
exposure. The 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 the volumetric point clouds to
approximate the surface, guided by two novel geometric constraints: sign
consistency constraint and on-surface 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 robustness of our method
to noise distribution and its potential to improve segmentation performance.
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