Robust Deep Feature Ultrasound Image-Based Visual Servoing: Focus on Cardiac Examination

Abstract

This article introduces a robust deep feature ultrasound image-based visual servoing (UIBVS) technique for an ultrasound robot focusing on automatic cardiac examination. To this end, a convolutional neural network named ultrasound-cardiac-feature-net (UCF-Net) is developed, which is trained in a supervised manner to process ultrasound images and generate a set of six image features referred to as deep ultrasound image features. To enhance the robustness of UCF-Net against the variables that affect the ultrasound image quality, such as interaction normal force, scan depth, dynamic range, power, and gain, several datasets with different sets of parameters are gathered for training. Deep ultrasound image features enable an eye-in-hand robot to interact with the human body through UIBVS. To implement UIBVS, a filtered integral quasi-super-twisting algorithm (FIQSTA) is synthesized as the primary controller. Interaction force control is also considered within a hybrid vision/force control framework, providing compliance with the body and increasing the safety of the interaction. The proof of the robustness and stability of FIQSTA is also investigated. Experimental results on a cardiac phantom for four main views, i.e., parasternal short axis, parasternal long axis, subcostal, and apical four chambers views, and a trajectory passing through the main views demonstrate the feasibility of the proposed method for cardiac examination and the superior performance of the main controller to other well-known methods, including proportional (P) controller, sliding mode controller, super-twisting algorithm (STA), and integral quasi-STA.