Identifying visible tissue in intraoperative ultrasound: a method and application
This work addresses a demanding visuotactile task in medical imaging to support clinical training and robotic ultrasound automation, representing an incremental improvement with specific gains.
The paper tackles the challenge of identifying visible tissue in intraoperative ultrasound by proposing an iterative filtering and topological method, which achieves superior performance with an F_beta score of 0.864 on in vivo data and an average normalized root mean square error of 0.168 on phantom data compared to existing methods.
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, whilst 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 score of 0.864, in comparison to 0.838, 0.808, 0.808. A novel framework for evaluating the confidence estimation of probe tissue contact is created. The phantom data is 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 to 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.