In this work, two deep learning models, trained to segment the liver and perform depth reconstruction, are compared and analysed with their post-hoc explanation interplay. The first model (a U-Net) is designed to perform liver semantic segmentation over different subjects and scenarios. Particularly, the image pixels representing the liver are classified and separated by the surrounding pixels. Meanwhile, with the second model, a depth estimation is performed to regress the z-position of each pixel (relative depths). In general, these two models apply a sort of classification task which can be explained for each model individually and that can be combined to show additional relations and insights between the most relevant learned features. In detail, this work shows how post-hoc explainable AI systems (X-AI) based on Grad CAM and Grad CAM++ can be compared by introducing Cross X-AI (CX-AI). Typically the post-hoc explanation maps provide different visual explanations of their decisions based on the two proposed approaches. Our results show that the Grad Cam++ segmentation explanation maps present cross-learning strategies similar to disparity explanations (and vice versa).

In this paper, control strategies for an active stabilizer dedicated to beating heart coronary artery bypass grafting are investigated. The active stabilizer, which consists of a piezoactuated compliant mechanism, has to be controlled to compensate for the displacements induced by the beating heart in order to provide the surgeon with a locally motionless myocardium surface. Three controllers, including different levels of prior knowledge about the heart motion, are presented. Their performance with respect to modeling uncertainties, arising unknown interactions of the stabilizer with its positioning mechanism, and the heart, is studied through simulations, as well as laboratory and in vivo experiments. Finally, the selection of the most adequate control scheme and the performance of the device from a clinical point of view are discussed.

Off-pump coronary artery bypass grafting (CABG) is probably an important milestone in the cardiac surgery history. This technique is currently made possible thanks to the use of mechanical stabilizers. Nevertheless, the available stabilizers are not fully satisfying. Indeed, they have been demonstrated to exhibit significant residual motion. In this paper we propose a novel piezo-actuated compliant stabilizer, whose architecture is compatible with a minimally invasive surgery context. This active stabilizer is controlled using high speed visual feedback in order to compensate in real time for the residual cardiac motion. In vivo experimental results using the developed prototype are given to demonstrate the efficiency of the adopted approach.