No abstract

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy.

An algorithm that presents the best possible approximation for the theoretical Bézier curve and the real path on which a mobile robot moves in a dynamic environment with mobile obstacles and boundaries is introduced in this paper. The algorithm is tested on a set of scenarios that comprehensively cover critical situations of obstacle avoidance. The selection of scenarios is made by deploying robot navigation performances into constraints and further into descriptive characteristics of the scenarios. Computer-simulated environments are created with dedicated tools (i.e., Gazebo) and modeling and programming technologies (i.e., Robot Operating System (ROS) and Python). It is shown that the proposed algorithm improves the performance of the path for robot navigation in a highly dynamic environment, with dense mobile obstacles.