Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints

TL;DR

This paper presents FUNSR, a self-supervised neural implicit method for reconstructing high-quality, smooth 3D surfaces from freehand ultrasound volumes using geometric constraints, improving accuracy and robustness.

Contribution

The study introduces a novel self-supervised neural implicit surface reconstruction technique with geometric constraints for freehand 3D ultrasound data, enhancing surface quality and robustness.

Findings

Effective across diverse anatomical datasets

Produces smooth, continuous surface representations

Improves segmentation performance and noise robustness

Abstract

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.