UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
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).
