Robotic Intra-Operative Ultrasound:Virtual Environments and Parallel Systems

Robotic intra-operative ultrasound has the potential to improve the conventional practice of diagnosis and procedure guidance that are currently performed manually.Working towards automatic or semi-automatic ultrasound,being able to define ultrasound views and the corresponding probe poses via intelligent approaches become crucial.Based on the concept of parallel system which incorporates the ingredients of artificial systems,computational experiments,and parallel execution,this paper utilized a recent developed robotic trans-esophageal ultrasound system as the study object to explore the method for developing the corresponding virtual environments and present the potential applications of such systems.The proposed virtual system includes the use of 3 D slicer as the main workspace and graphic user interface(GUI),Matlab engine to provide robotic control algorithms and customized functions,and PLUS(Public software Library for Ultra Sound imaging research)toolkit to generate simulated ultrasound images.Detailed implementation methods were presented and the proposed features of the system were explained.Based on this virtual system,example uses and case studies were presented to demonstrate its capabilities when used together with the physical TEE robot.This includes standard view definition and customized view optimization for pre-planning and navigation,as well as robotic control algorithm evaluations to facilitate real-time automatic probe pose adjustments.To conclude,the proposed virtual system would be a powerful tool to facilitate the further developments and clinical uses of the robotic intra-operative ultrasound systems.

An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs

A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease. Analysis of CT signs is helpful to understand the pathological origin of the lesion. In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately. To this end, we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs. We first construct a Convolutional Neural Network(CNN) classifier adopting Inception modules and pre-train it on ImageNet. We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets, and fuse these 10 classifiers with an artificial immune ensemble algorithm. The overall sensitivity, specificity, and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion(AIA-INF) algorithm are 82.22%, 93.17%, and 88.67%, respectively, which are significantly higher than those of the alternative Bagging and Boosting methods. The experimental results show that our Inception-based ensemble classifier offers promising performance, and compared with other CADx systems, this scheme can offer a more detailed reference for diagnosis, and can be valuable for junior radiologist training.