Assessing performance of augmented reality-based neurosurgical training

This paper presents a novel augmented reality(AR)-based neurosurgical training simulator which provides a very natural way for surgeons to learn neurosurgical skills.Surgical simulation with bimanual haptic interaction is integrated in this work to provide a simulated environment for users to achieve holographic guidance for pre-operative training.To achieve the AR guidance,the simulator should precisely overlay the 3D anatomical information of the hidden target organs in the patients in real surgery.In this regard,the patient-specific anatomy structures are reconstructed from segmented brain magnetic resonance imaging.We propose a registration method for precise mapping of the virtual and real information.In addition,the simulator provides bimanual haptic interaction in a holographic environment to mimic real brain tumor resection.In this study,we conduct AR-based guidance validation and a user study on the developed simulator,which demonstrate the high accuracy of our AR-based neurosurgery simulator,as well as the AR guidance mode’s potential to improve neurosurgery by simplifying the operation,reducing the difficulty of the operation,shortening the operation time,and increasing the precision of the operation.

Multi-block SSD based on small object detection for UAV railway scene surveillance

A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance.To address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is designed.First,the original input images are segmented into several overlapped patches.Second,each patch is separately fed into an SSD to detect the objects.Third,the patches are merged together through two stages.In the first stage,the truncated object of the sub-layer detection result is spliced.In the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers.The boxes that are not detected in the main-layer are retained.In addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this issue.The deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case study.Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.