# Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints Source: [Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints]() Collection index: [llms.txt]() Data updated: 2025-10-26T06:56:30.471Z Authors: Hongbo Chen, Logiraj Kumaralingam, Shuhang Zhang, Sheng Song, Fayi Zhang, Haibin Zhang, Thanh-Tu Pham, Edmond H. M. Lou, Kumaradevan Punithakumar, Yuyao Zhang, Lawrence H. Le, Rui Zheng Published: 2024-01-11T13:49:51.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Neural shape representation on UT1 Scans (test)](): Neural Shape Representation; UT1 Scans (test); CD (mm): 2.11. - [Neural shape representation on UT2 Scans (test)](): Neural Shape Representation; UT2 Scans (test); Chamfer Distance (mm): 2.4. ## Original sources and implementation [Original paper]() [PDF]()