Regularising disparity estimation via multi task learning with structured light reconstruction

3D reconstruction is a useful tool for surgical planning and guidance. However, the lack of available medical data stunts research and development in this field, as supervised deep learning methods for accurate disparity estimation rely heavily on large datasets containing ground truth information. Alternative approaches to supervision have been explored, such as self-supervision, which can reduce or remove entirely the need for ground truth. However, no proposed alternatives have demonstrated performance capabilities close to what would be expected from a supervised setup. This work aims to alleviate this issue. In this paper, we investigate the learning of structured light projections to enhance the development of direct disparity estimation networks. We show for the first time that it is possible to accurately learn the projection of structured light on a scene, implicitly learning disparity. Secondly, we \textcolor{black}{explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity. We present results which show that MTL with structured light improves disparity training; without increasing the number of model parameters. Our MTL setup outperformed the single task learning (STL) network in every validation test. Notably, in the medical generalisation test, the STL error was 1.4 times worse than that of the best MTL performance. The benefit of using MTL is emphasised when the training data is limited.} A dataset containing stereoscopic images, disparity maps and structured light projections on medical phantoms and ex vivo tissue was created for evaluation together with virtual scenes. This dataset will be made publicly available in the future

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