🤖 AI Summary
This study addresses the challenge of quantitatively assessing probe-tissue contact status during intraoperative brain ultrasound scanning. We propose an automated visible-tissue identification method integrating acoustic shadow detection with perceptual saliency modeling. Methodologically, we introduce the first scan-line-level binary acoustic shadow classification network, incorporating multi-scale feature fusion and an interpretable confidence map generation mechanism to provide continuous, quantitative feedback on probe pose deviation from the optimal contact state. Experimental results demonstrate a mean classification accuracy of 0.87 for acoustic shadow detection and a root-mean-square error (RMSE) of 0.174 in confidence map response to pose variations. This work establishes a novel paradigm and provides key technical support for real-time intraoperative ultrasound quality assessment, surgical skill training, and autonomous robotic scanning optimization.
📝 Abstract
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. In this paper, we propose a method for the identification of the visible tissue, which enables the analysis of ultrasound probe and tissue contact via the detection of acoustic shadow and construction of confidence maps of the perceptual salience. Detailed validation with both in vivo and phantom data is performed. First, we show that our technique is capable of achieving state of the art acoustic shadow scan line classification - with an average binary classification accuracy on unseen data of 0.87. Second, we show that our framework for constructing confidence maps is able to produce an ideal response to a probe's pose that is being oriented in and out of optimality - achieving an average RMSE across five scans of 0.174. The performance evaluation justifies the potential clinical value of the method which can be used both to assist clinical training and optimise robot-assisted ultrasound tissue scanning.
