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.

翻译：术中超声扫描是一项要求较高的视觉触觉任务。操作者需同时定位超声视角，手动微调探头姿态，确保既不施加过大压力也不与组织脱离接触，同时还要对可见组织进行特征描述。本文提出一种可见组织识别方法，通过检测声学阴影并构建感知显著性的置信度图，实现超声探头与组织接触状态的分析。我们利用活体与体模数据进行了详细验证。首先，本技术在声学阴影扫描线分类中达到当前最优水平——对未见数据的平均二分类准确率为0.87。其次，我们构建的置信度图框架能对探头朝向最优方向的变化产生理想响应——在五次扫描中平均均方根误差为0.174。性能评估证实了该方法潜在的临床价值，既可辅助临床培训，也可优化机器人辅助超声组织扫描。