Identifying Visible Tissue in Intraoperative Ultrasound Images during Brain Surgery: A Method and Application

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

Ultrasound imaging operation capture and image analysis for speckle noise reduction and detection of shadows

Ultrasound is becoming increasingly important in medicine, both as a diagnostic tool and as a therapeutic modality. At present, experienced sonographers observe trainees as they generate hundreds of images, constantly providing them feedback and eventually deciding if they have the appropriate skills and knowledge to perform ultrasound independently. This research seeks to advance towards developing an automated system capable of assessing the motion of an ultrasound transducer and differentiate between a novice, an intermediate and an expert sonographer. The research in this thesis synchronizes the ultrasound images with three depth sensors (Microsoft Kinect) placed on the top, left and right side of the patient to ensure the visibility of the ultrasound probe. Videos obtained from the three categories of sonographers are manually labeled and compared using Studiocode Development Environment to complete the items on the medical form checklist. Next, this thesis investigates and applies well known techniques used to smooth and suppress speckle noise in ultrasound images by using quality metrics to test their performance and show the benefits each one can contribute. Finally, this thesis investigates the problem of shadow detection in ultrasound imaging and proposes to detect shadows automatically with an ultrasound confidence map using a random walks algorithm. The results show that the proposed algorithm achieves an accuracy of automatic detection of up to 85%, based on both the expert and manual segmentation