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

arXiv: https://arxiv.org/abs/2401.05915 · HTML: https://mlsanity.com/paper/2401.05915/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2024.

## Built on by (most cited first, 15 of 15)

- [SpINR: Neural Volumetric Reconstruction for FMCW Radars](https://mlsanity.com/paper/2503.23313/) (2025)
- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [TCFNet: Bidirectional face-bone transformation via a Transformer-based coarse-to-fine point movement network](https://mlsanity.com/paper/2508.14373/) (2025)
- [UltrON: Ultrasound Occupancy Networks](https://mlsanity.com/paper/2509.08991/) (2025)
- [MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging](https://mlsanity.com/paper/2509.16806/) (2025)
- [Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep Learning](https://mlsanity.com/paper/2502.03505/) (2025)
- [UltraBoneUDF: Self-supervised Bone Surface Reconstruction from Ultrasound Based on Neural Unsigned Distance Functions](https://mlsanity.com/paper/2505.17912/) (2025)
- [SpINRv2: Implicit Neural Representation for Passband FMCW Radars](https://mlsanity.com/paper/2506.08163/) (2025)
- [SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar](https://mlsanity.com/paper/2509.11087/) (2025)
- [UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction](https://mlsanity.com/paper/2502.03783/) (2025)
- [ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound](https://mlsanity.com/paper/2503.06686/) (2025)
- [NeuralBoneReg: An Instance-Specific Label-Free Point Cloud-Based Method for Multi-Modal Bone Surface Registration](https://mlsanity.com/paper/2511.14286/) (2025)
- [KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks](https://mlsanity.com/paper/2602.13299/) (2026)
- [High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approach](https://mlsanity.com/paper/2606.17836/) (2026)
- [UBone3D: Physics-Rectified Conditional Flow Matching for Anatomical 3D Shape Completion from Ultrasound](https://mlsanity.com/paper/2609.11506/) (2026)

## Abstract (arXiv)

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.