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

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).

UltraBones100k: An Ultrasound Image Dataset with CT-Derived Labels for Lower Extremity Long Bone Surface Segmentation

Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, and acoustic shadowing, which make interpretation difficult. Existing deep learning models for bone segmentation rely primarily on costly manual labeling by experts, limiting dataset size and model generalizability. Additionally, the complexity of ultrasound physics and acoustic shadow makes the images difficult for humans to interpret, leading to incomplete labels in anechoic regions and limiting model performance. To advance ultrasound bone segmentation and establish effective model benchmarks, larger and higher-quality datasets are needed. We propose a methodology for collecting ex-vivo ultrasound datasets with automatically generated bone labels, including anechoic regions. The proposed labels are derived by accurately superimposing tracked bone CT models onto the tracked ultrasound images. These initial labels are refined to account for ultrasound physics. A clinical evaluation is conducted by an expert physician specialized on orthopedic sonography to assess the quality of the generated bone labels. A neural network for bone segmentation is trained on the collected dataset and its predictions are compared to expert manual labels, evaluating accuracy, completeness, and F1-score. We collected the largest known dataset of 100k ultrasound images of human lower limbs with bone labels, called UltraBones100k. A Wilcoxon signed-rank test with Bonferroni correction confirmed that the bone alignment after our method significantly improved the quality of bone labeling (p < 0.001). The model trained on UltraBones100k consistently outperforms manual labeling in all metrics, particularly in low-intensity regions (320% improvement in completeness at a distance threshold of 0.5 mm).