Purpose: Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe, making sure not to apply excessive force or breaking contact with the tissue, while also characterising the visible tissue. Method: To analyse the probe-tissue contact, an iterative filtering and topological method is proposed to identify the underlying visible tissue, which can be used to detect acoustic shadow and construct confidence maps of perceptual salience. Results: For evaluation, datasets containing both in vivo and medical phantom data are created. A suite of evaluations is performed, including an evaluation of acoustic shadow classification. Compared to an ablation, deep learning, and statistical method, the proposed approach achieves superior classification on in vivo data, achieving an <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11548_2025_3415_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">$F_{\beta }$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>F</mi> <mi>β</mi> </msub> </math></EquationSource> </InlineEquation> score of 0.864, in comparison with 0.838, 0.808, and 0.808. A novel framework for evaluating the confidence estimation of probe-tissue contact is created. The phantom data are captured specifically for this, and comparison is made against two established methods. The proposed method produced the superior response, achieving an average normalised root-mean-square error of 0.168, in comparison with 1.836 and 4.542. Evaluation is also extended to determine the algorithm’s robustness to parameter perturbation, speckle noise, data distribution shift, and capability for guiding a robotic scan. Conclusion: The results of this comprehensive set of experiments justify the potential clinical value of the proposed algorithm, which can be used to support clinical training and robotic ultrasound automation.

Identifying visible tissue in intraoperative ultrasound: a method and application

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2025年6月28日

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Alistair Weld,
Luke Dixon,
Michael Dyck,
Giulio Anichini,
Alex Ranne,
Sophie Camp,
Stamatia Giannarou

摘要

Purpose:

Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe, making sure not to apply excessive force or breaking contact with the tissue, while also characterising the visible tissue.

Method:

To analyse the probe-tissue contact, an iterative filtering and topological method is proposed to identify the underlying visible tissue, which can be used to detect acoustic shadow and construct confidence maps of perceptual salience.

Results:

For evaluation, datasets containing both in vivo and medical phantom data are created. A suite of evaluations is performed, including an evaluation of acoustic shadow classification. Compared to an ablation, deep learning, and statistical method, the proposed approach achieves superior classification on in vivo data, achieving an $F_{\beta }$ $F_{β}$ score of 0.864, in comparison with 0.838, 0.808, and 0.808. A novel framework for evaluating the confidence estimation of probe-tissue contact is created. The phantom data are captured specifically for this, and comparison is made against two established methods. The proposed method produced the superior response, achieving an average normalised root-mean-square error of 0.168, in comparison with 1.836 and 4.542. Evaluation is also extended to determine the algorithm’s robustness to parameter perturbation, speckle noise, data distribution shift, and capability for guiding a robotic scan.

Conclusion:

The results of this comprehensive set of experiments justify the potential clinical value of the proposed algorithm, which can be used to support clinical training and robotic ultrasound automation.