UltraBoneUDF: Self-supervised Bone Surface Reconstruction from Ultrasound Based on Neural Unsigned Distance Functions

2025-05-23

AI Summary

In ultrasound-guided orthopedic surgery, conventional B-mode ultrasound yields sparse and incomplete bone surface data, leading to severe geometric distortion and prominent artifacts in existing 3D reconstruction methods—especially for thin, open bone structures. To address this, we propose a registration-free and annotation-free framework for continuous bone surface reconstruction. Our key contributions are: (1) the first ultrasound-specific global feature extractor; (2) a self-supervised loss function based on local tangent plane optimization, enhancing robustness for open-surface reconstruction; and (3) seamless integration of neural unsigned distance fields (Neural UDFs) with ultrasound image features. Evaluated on four public datasets, our method achieves a Chamfer distance error of only 0.05 mm—outperforming state-of-the-art approaches by up to 69.3%. To our knowledge, this is the first work enabling high-fidelity, ultrasound-driven 3D reconstruction of open bone surfaces.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Feature Construction/Reformulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Background: Bone surface reconstruction plays a critical role in computer-assisted orthopedic surgery. Compared to traditional imaging modalities such as CT and MRI, ultrasound offers a radiation-free, cost-effective, and portable alternative. Continuous bone surface reconstruction can be employed for many clinical applications. However, due to the inherent limitations of ultrasound imaging, B-mode ultrasound typically capture only partial bone surfaces. Existing reconstruction methods struggle with such incomplete data, leading to artifacts and increased reconstruction errors. Effective techniques for accurately reconstructing thin and open bone surfaces from real-world 3D ultrasound volumes remain lacking. Methods: We propose UltraBoneUDF, a self-supervised framework designed for reconstructing open bone surfaces from ultrasound using neural Unsigned Distance Functions. To enhance reconstruction quality, we introduce a novel global feature extractor that effectively fuses ultrasound-specific image characteristics. Additionally, we present a novel loss function based on local tangent plane optimization that substantially improves surface reconstruction quality. UltraBoneUDF and baseline models are extensively evaluated on four open-source datasets. Results: Qualitative results highlight the limitations of the state-of-the-art methods for open bone surface reconstruction and demonstrate the effectiveness of UltraBoneUDF. Quantitatively, UltraBoneUDF significantly outperforms competing methods across all evaluated datasets for both open and closed bone surface reconstruction in terms of mean Chamfer distance error: 1.10 mm on the UltraBones100k dataset (39.6% improvement compared to the SOTA), 0.23 mm on the OpenBoneCT dataset (69.3% improvement), 0.18 mm on the ClosedBoneCT dataset (70.2% improvement), and 0.05 mm on the Prostate dataset (55.3% improvement).

Problem

• Reconstructing bone surfaces from incomplete ultrasound data
• Addressing artifacts in bone surface reconstruction methods
• Improving accuracy for thin and open bone surfaces

Innovation

• Self-supervised bone reconstruction via neural UDF
• Global feature extractor fuses ultrasound characteristics
• Novel loss function optimizes local tangent planes