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From AR to AI: Augmentation Technology for Intelligent Surgery and Medical Treatments

IFAC-PapersOnLine 2020年第5期53卷 792-796页

作者： Mei Zhang Zhicheng Zhang Xiao Wang Hui Yu Yifan Xia Kanran Tan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Qingdao Academy of Intelligent Industries Qingdao 266109 China The Department of the 7th Medical Center of PLA General Hospital Beijing 100700 China The Department of Computer Science Purdue University West Lafayette Indiana 47906 USA The School of Creative Technologies University of Portsmouth UK The Department of Computer Science Johns Hopkins University Baltimore Maryland USA

With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more information beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly determine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly deformable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency.

关键词： Potential impact of automation open problems Augmented reality Medical treatment Parallel theory 3D technology

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MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1156-1175页

作者： Chang, Cheng Cao, Dongpu Chen, Long Su, Kui Su, Kuifeng Su, Yuelong Wang, Fei-Yue Wang, Jue Wang, Ping Wei, Junqing Wu, Gansha Wu, Xiangbin Xu, Huile Zheng, Nanning Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Waterloo Dept Mech & Mech Engn Waterloo ON N2L 3G1 Canada Waytous Inc Beijing 100083 Peoples R China Alibaba DAMO Acad Autonomous Driving Hangzhou 311121 Peoples R China Tencent Autonomous Driving Beijing 100084 Peoples R China AutoNavi Software Co Traffic Management Solut Div Beijing 100102 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Sys Inst Automat Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Peking Univ Beijing 100084 Peoples R China DiDi Autonomous Driving Co Beijing 100094 Peoples R China Uisee Technol Beijing Co Ltd Beijing 100028 Peoples R China Intel Labs China Beijing 100190 Peoples R China Chinese Acad Sci Momenta & Natl Lab Pattern Recognit Inst Automat Beijing 100084 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot IAIR Xian 710049 Peoples R China Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China

Autonomous driving related researches require the analysis and usage of massive amounts of driving scenario data. Compared to raw data collected by sensors, scenario data provide a preliminary abstraction of driving tasks and processes, explicitly integrate information about the road environment and the dynamic and static attributes of traffic participants, making it easier to conduct task understanding and decision making. However, many existing driving scenario datasets have the following two problems. First, it is not clear which data fields need to be recorded for driving scenarios. The data storage formats and organization standards are inconsistent. Second, the datasets cannot establish driving scenario indexing effectively. Existing datasets are sparsely annotated and difficult to index, which is detrimental to data sampling and extraction for machine learning process, thus hindering efficient fusion and reuse. In this paper, we propose MetaScenario, a framework for driving scenario data. We describe driving scenarios and design the centralized and unified data framework for the storage, processing, and indexing of scenario data based on relational database. The concept of atom scenario is proposed and characterized using semantic graphs. We also annotate and classify behaviors and interactions of traffic participants in atom scenarios by extracting the spatiotemporal evolution of semantic information. The annotation facilitates the indexing and extraction of data. The scenario datasets are further evaluated via the data distribution and annotation statistics. MetaScenario can provide researchers with convenient tools for scenario data extraction and important analytical references.

关键词： Cameras Laser radar Autonomous vehicles Roads Annotations Trajectory Indexing Driving scenario data storage data indexing

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Ferrimagnetic Vortex Nanoring-Mediated Mild Magnetic Hyperthermia Imparts Potent Immunological Effect for Treating Cancer Metastasis

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ACS NANO 2019年第8期13卷 8811-8825页

作者： Liu, Xiaoli Zheng, Jianjun Sun, Wei Zhao, Xiao Li, Yao Gong, Ningqiang Wang, Yanyun Ma, Xiaowei Zhang, Tingbin Zhao, Ling-Yun Hou, Yayi Wu, Zhibing Du, Yang Fan, Haiming Tian, Jie Liang, Xing-Jie Natl Ctr Nanosci & Technol China CAS Ctr Excellence Nanosci CAS Key Lab Biomed Effects Nanomat & Nanosafety 11First North Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Northwest Univ Coll Life Sci Xian 710069 Shaanxi Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Northwest Univ Coll Chem & Mat Sci Key Lab Synthet & Nat Funct Mol Chem Minist Educ Xian 710127 Shaanxi Peoples R China Univ Chinese Acad Sci Ningbo 2 Hosp Hwa Mei Hosp Dept Radiol Ningbo 315010 Zhejiang Peoples R China Tsinghua Univ Sch Mat Sci & Engn Key Lab Adv Mat Beijing 100084 Peoples R China Nanjing Univ Med Sch Div Immunol State Key Lab Pharmaceut Biotechnol Nanjing 210093 Jiangsu Peoples R China Nanjing Univ Jiangsu Key Lab Mol Med Nanjing 210093 Jiangsu Peoples R China Zhejiang Hosp Dept Radiat Oncol Hangzhou 310013 Zhejiang Peoples R China

Cancer metastasis is a serious concern and a major reason for treatment failure. Herein, we have reported the development of an effective and safe nanotherapeutic strategy that can eradicate primary tumors, inhibit metastasizing to lung, and control the metastasis and growth of distant tumors. Briefly, ferrimagnetic vortex-domain iron oxide nanoring (FVIO)-mediated mild magnetic hyperthermia caused calreticulin (CRT) expression on the 4T1 breast cancer cells. The CRT expression transmitted an "eat-me" signal and promoted phagocytic uptake of cancer cells by the immune system to induce an efficient immunogenic cell death, further leading to the macrophage polarization. This mild thermotherapy promoted 88% increase of CD8(+) cytotoxic T lymphocyte infiltration in distant tumors and triggered immunotherapy by effectively sensitizing tumors to the PD-L1 checkpoint blockade. The percentage of CD8(+) cytotoxic T lymphocytes can be further increased from 55.4% to 64.5% after combining with PD-L1 blockade. Moreover, the combination treatment also inhibited the immunosuppressive response of the tumor, evidenced by significant down-regulation of myeloid-derived suppressor cells (MDSCs). Our results revealed that the FWD-mediated mild magnetic hyperthermia can activate the host immune systems and efficiently cooperate with PD-L1 blockade to inhibit the potential metastatic spreading as well as the growth of distant tumors.

关键词： ferrimagnetic vortex-domain iron oxide nanorings mild magnetic hyperthermia PD-L1 blockade immunotherapy immunological effect cancer metastasis

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FusionPlanner: A multi-task motion planner for mining trucks via multi-sensor fusion

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MECHANICAL systems AND SIGNAL PROCESSING 2024年 208卷

作者： Teng, Siyu Li, Luxi Li, Yuchen Hu, Xuemin Li, Lingxi Ai, Yunfeng Chen, Long Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China BNU HKBU United Int Coll Dept Comp Sci Zhuhai Peoples R China Hubei Univ Sch Artificial Intelligence Wuhan Peoples R China Waytous Inc Beijing Peoples R China IUPUI Dept Elect & Comp Engn Indianapolis IN USA Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Mulimodal Artificial Intelligence S Beijing Peoples R China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open -pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open -pit mines is proposed in this research. Firstly, we propose a multi -task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the multi -sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well -trained algorithms in transportation roads of open -pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open -pit mining scenarios. PMS enables the users to manage and control open -pit mine transportation from both the single -truck control and multi -truck scheduling perspectives. The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.

关键词： Autonomous driving Motion planning Simulation Multi-task Multi-sensor

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Sora for Smart Mining: Towards Sustainability With Imaginative Intelligence and Parallel Intelligence

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4577-4578页

作者： Xie, Yuting Wang, Cong Liu, Kunhua Zhe, Xuanyuan He, Yuhang Cheng, Hui Nuechter, Andreas Li, Lingxi Huai, Rouxing Tang, Shuming Ma, Siji Chen, Long Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China Qingdao Univ Technol Sch Mech & Automot Engn Qingdao 266520 Peoples R China Beijing Normal Univ Kong Baptist Univ United Int Guangdong Prov Key Lab Interdisciplinary Res & Ap Zhuhai 519087 Peoples R China Univ Oxford Dept Comp Sci Oxford OX1 2JD England Univ Wurzburg Comp Sci Robot D-97070 Wurzburg Germany Indiana Univ Purdue Univ Indianapolis IN 46202 USA Beijing Huairou Acad Parallel Sensing Beijing 101499 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Waytous Co Ltd Beijing 100083 Peoples R China Jiaotong Univ Coll Artificial Intelligence Xian 710049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This letter summarizes discussions from IEEE TIV's Autonomous Mining Workshop, emphasizing the potential of video generation models in advancing smart mining.

关键词： Computational modeling Artificial intelligence Intelligent vehicles Data mining Visualization Electronic mail Collaboration Smart mining video generation models sustainable mining intelligent vehicles

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Design and Visual Servoing control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

arXiv

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arXiv 2024年

作者： Chen, Jian Chen, Mingcong Zhao, Qingxiang Wang, Shuai Wang, Yihe Xiao, Ying Hu, Jian Chan, Danny Tat Ming Yeung, Kam Tong Leo Chan, David Yuen Chung Liu, Hongbin The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Beijing100190 China Hong Kong Department of Biomedical Engineering City University of Hong Kong Hong Kong CAIR-HKISI-CAS HKSAR China The State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing100190 China Department of Surgery The Chinese University of Hong Kong Hong Kong The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing100190 China The School of Biomedical Engineering and Imaging Sciences King's College London LondonSE1 7EU United Kingdom

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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Unsupervised Domain Adaptation for Remote Sensing Image Segmentation Based on Adversarial Learning and Self-Training

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Liang, Chenbin Cheng, Bo Xiao, Baihua Dong, Yunyun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Shaanxi Normal Univ Northwest Land & Resources Res Ctr Xian 710119 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Shaanxi Normal Univ Northwest Land & Resource Res Ctr Xian 710000 Peoples R China

There is a large amount of out-of-distribution (OOD) data in remote sensing, which hinders high-accuracy segmentation models under the assumption of independent identical distribution (i.i.d.) from stable and reliable performance in real-world remote sensing applications. And domain adaptation (DA) is presented to seamlessly extend classifiers to the label-scarce target domain in the presence of the label-sufficient source domain with different data distributions. However, given that the domain shift, i.e., the distribution difference between the two domains, is more serious in remote sensing images, the current DA methods for image segmentation in computer vision (CV) typically perform unsatisfactorily in remote sensing, even suffering from the negative domain alignment. To this end, this letter proposes the self-training adversarial DA (STADA) method for remote sensing image segmentation, which not only performs adversarial learning to extract domain-invariant features but also implements self-training using pseudo-labels in the target domain denoised by the conditional adversarial loss for classifier adaptation. The International Society for Photogrammetry and Remote Sensing (ISPRS) and Wuhan University (WHU) datasets are employed to conduct extensive experiments to investigate the effectiveness of STADA and the specific effect of each DA component. And the experimental results demonstrate that STADA outperforms other state-of-the-art DA methods in the remote sensing image segmentation task.

关键词： Adversarial learning domain adaptation (DA) self-training semantic segmentation

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A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration

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REMOTE SENSING 2023年第13期15卷 3243页

作者： Liang, Chenbin Dong, Yunyun Zhao, Changjun Sun, Zengguo Shaanxi Normal Univ Northwest Land & Resource Res Ctr Xian 710119 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Feature matching is a core step in multi-source remote sensing image registration approaches based on feature. However, for existing methods, whether traditional classical SIFT algorithm or deep learning-based methods, they essentially rely on generating descriptors from local regions of feature points, which can lead to low matching success rates due to various challenges, including gray-scale changes, content changes, local similarity, and occlusions between images. Inspired by the human approach of finding rough corresponding regions globally and then carefully comparing local regions, and the excellent global attention property of transformers, the proposed feature matching network adopts a coarse-to-fine matching strategy that utilizes both global and local information between images to predict corresponding feature points. Importantly, the network has great flexibility of matching corresponding points for any feature points and can be effectively trained without strong supervised signals of corresponding feature points and only require the true geometric transformation between images. The qualitative experiment illustrate the effectiveness of the proposed network by matching feature points extracted by SIFT or sampled uniformly. In the quantitative experiments, we used feature points extracted by SIFT, SuperPoint, and LoFTR as the keypoints to be matched. We then calculated the mean match success ratio (MSR) and mean reprojection error (MRE) of each method at different thresholds in the test dataset. Additionally, boxplot graphs were plotted to visualize the distributions. By comparing the MSR and MRE values as well as their distributions with other methods, we can conclude that the proposed method consistently outperforms the comparison methods in terms of MSR at different thresholds. Moreover, the MSR of the proposed method remains within a reasonable range compared to the MRE of other methods.

关键词： feature matching image registration coarse-to-fine matching transformer deep learning

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The process of 3D printed skull models for anatomy education

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COMPUTER ASSISTED SURGERY 2019年第Supp 1期24卷 121-130页

作者： Shen, Zhen Yao, Yong Xie, Yi Guo, Chao Shang, Xiuqin Dong, Xisong Li, Yuqing Pan, Zhouxian Chen, Shi Xiong, Gang Wang, Fei-Yue Pan, Hui Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Chinese Acad Med Sci Peking Union Med Coll Hosp PUMCH Dept Neurosurg Beijing Peoples R China Peking Union Med Coll Beijing Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China CAMS PUMCH Beijing Peoples R China PUMC Eight Year Program Clin Med Beijing Peoples R China CAMS PUMCH Minist Hlth Dept EndocrinolEndocrine Key Lab Beijing Peoples R China PUMC Beijing Peoples R China CAMS PUMCH Natl Virtual Simulat Lab Educ Ctr Med Sci Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan Peoples R China CAMS PUMCH Dept Educ Beijing Peoples R China

In general, the 3 D printed medical models are made based on virtual digital models obtained from machines such as the computed tomography scanner. However, due to the limited accuracy of CT scanning technology, which is usually 1 millimeter, there are differences between scanned results and the real structure. Besides, the collected data can hardly be printed directly because of some errors in the model. In this paper, we present a general and efficient procedure to process the digital skull data to make the printed structures meet the requirements of anatomy education, which combines the use of five 3 D manipulation tools and the procedure can be finished within 6 hours. Then the model is printed and compared with the cadaveric skull from frontal, left, right and anterior views respectively. The printed model can describe the correct structure and details of the skull clearly, which can be considered as a good alternative to the cadaveric skull. The manipulation procedure presented in this study is an easily available and cost-effective way to obtain a printed skull model from the original CT data, which has a considerable economic and social benefit for the medical education. The steps of the data processing can be performed easily. The cost for the 3 D printed model is also low. Outcomes of this study can be applied widely in processing skull data.

关键词： Skull model 3D printing Anatomy education

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LOCAL PECULIARITY ORIENTED DATA MINING AND ITS APPLICATION IN OUTLIER DETECTION

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2012年第6期11卷 1155-1181页

作者： Yang, Jian Zhong, Ning Yao, Yiyu Wang, Jue Beijing Univ Technol Int WIC Inst Beijing Peoples R China Maebashi Inst Technol Dept Life Sci & Informat Maebashi Gunma Japan Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Peculiarity oriented mining (POM), aimed at discovering peculiarity rules hidden in a dataset, is a data mining method. Peculiarity factor (PF) is one of the most important concepts in POM. In this paper, it is proved that PF can accurately characterize the peculiarity of data sampled from a normal distribution. However, for a general one-dimensional distribution, it does not have the property. A local version of PF, called LPF, is proposed to solve the difficulty. LPF can effectively describe the peculiarity of data sampled from a continuous one-dimensional distribution. Based on LPF, a framework of local peculiarity oriented mining is presented, which consists of two steps, namely, peculiar data identification and peculiar data analysis. Two algorithms for peculiar data identification and a case study of peculiar data analysis are given to make the framework practical. Experiments on several benchmark datasets show their good performance.

关键词： Data mining peculiarity factor local peculiarity factor local peculiarity oriented mining outlier detection

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