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Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Robust medical instrument segmentation challenge 2019

arXiv

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Roadmap on Quantum Thermodynamics

arXiv

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arXiv 2025年

作者： Campbell, Steve D’Amico, Irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, Simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.S. Busch, Thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, Salambô Deffner, Sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, Sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, Thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya S. Hernández-Gómez, Santiago Hu, Chang-Kang Iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel T. Lasek, Aleksander Lemziakov, Sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, Taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark T. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario Sánchez, Rafael Santos, Alan C. Sarthour, Roberto S. Sela, Eran Solfanelli, Andrea Souza, Alexandre M. Splettstoesser, Janine Tan, Dian Tesser, Ludovico Van Vu, Tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

The last two decades has seen quantum thermodynamics become a well established field of research in its own right. In that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. This Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, The Authors. All rights reserved.

关键词： Temperature

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Geometric Filterless Photodetectors for Mid-infrared Spin Light

arXiv

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arXiv 2022年

作者： Wei, Jingxuan Chen, Yang Li, Ying Li, Wei Xie, Junsheng Lee, Chengkuo Novoselov, Kostya S. Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei230026 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Haining314400 China GPL Photonics Lab State Key Laboratory of Applied Optics Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Center for Intelligent Sensors and MEMS National University of Singapore Singapore117608 Singapore Institute for Functional Intelligent Materials National University of Singapore Singapore117544 Singapore

Free-space circularly polarized light (CPL) detection, requiring polarizers and waveplates, has been well established, while such spatial degree of freedom is unfortunately absent in integrated on-chip optoelectronics. So far, those reported filterless CPL photodetectors suffer from the intrinsic small discrimination ratio, vulnerability to the non-CPL field components, and low responsivity. Here, we report a distinct paradigm of geometric photodetectors in mid-infrared exhibiting colossal discrimination ratio, close-to-perfect CPL-specific response, a zero-bias responsivity of 392 V/W at room temperature, and a detectivity of ellipticity down to 0.03o Hz−1/2. Our approach employs plasmonic nanostructures array with judiciously designed symmetry, assisted by graphene ribbons to electrically read their near-field optical information. This geometry-empowered recipe for infrared photodetectors provides a robust, direct, strict, and high-quality solution to on-chip filterless CPL detection and unlocks new opportunities for integrated functional optoelectronic devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Photodetectors

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A novel Skin lesion prediction and classification technique: ViT-GradCAM

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Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

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Corrigendum to “Road detection algorithm for Autonomous Navigation systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

引用

arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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ROLE OF SIMULATION IN RAPID PROTOTYPING FOR CONCEPT DEVELOPMENT

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 204-211页

作者： KING, JF BARTON, DE J. Fred King:is the manager of the Advanced Technology Department for Unisys in Reston Virginia. He earned his Ph.D. in mathematics from the University of Houston in 1977. He has been principal investigator of research projects in knowledge engineering pattern recognition and heuristic problem-solving. Efforts include the development of a multi-temporal multispectral classifier for identifying graincrops using LANDSAT satellite imagery data for NASA. Also as a member of the research team for a NCI study with Baylor College of Medicine and NASA he helped develop techniques for detection of carcinoma using multispectral microphotometer scans of lung tissue. He established and became technical director of the AI Laboratory for Ford Aerospace where he developed expert scheduling modeling and knowledge acquisition systems for NASA. Since joining Unisys in 1985 he has led the development of object-oriented programming environments blackboard architectures data fusion techniques using neural networks and intelligent data base systems. Douglas E. Barton:is manager of Logistics Information Systems for Unisys in Reston Virginia. He earned his B.A. degree in computer science from the College of William and Mary in 1978 and did postgraduate work in London as a Drapers Company scholar. Since joining Unisys in 1981 his work has concentrated on program management and software engineering of large scale data base management systems and design and implementation of knowledge-based systems in planning and logistics. As chairman of the Logistics Data Subcommittee of the National Security Industrial Association (NSIA) he led an industry initiative which examined concepts in knowledge-based systems in military logistics. His responsibilities also include evaluation development and tailoring of software engineering standards and procedures for data base and knowledge-based systems. He is currently program manager of the Navigation Information Management System which provides support to the Fleet Ballistic Missile Progr

A valuable technique during concept development is rapid prototyping of software for key design components. This approach is particularly useful when the optimum design approach is not readily apparent or several known alternatives need to be rapidly evaluated. A problem inherent in rapid prototyping is the lack of a "target system" with which to interface. Some alternatives are to develop test driver libraries, integrate the prototype with an existing working simulator, or build one for the specific problem. This paper presents a unique approach to concept development using rapid prototyping for concept development and scenario-based simulation for concept verification. The rapid prototyping environment, derived from artificial intelligence technology, is based on a blackboard architecture. The rapid prototype simulation capability is provided through an object-oriented modeling environment. It is shown how both simulation and blackboard technologies are used collectively to rapidly gain insight into a tenacious problem. A specific example will be discussed where this approach was used to evolve the logic of a mission controller for an autonomous underwater vehicle.

关键词： Military Engineering

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Roadmap on soft robotics: multifunctionality, adaptability and growth without borders

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Multifunctional Materials 2022年第3期5卷 032001-032001页

作者： Mazzolai, Barbara Mondini, Alessio Del Dottore, Emanuela Margheri, Laura Carpi, Federico Suzumori, Koichi Cianchetti, Matteo Speck, Thomas Smoukov, Stoyan K. Burgert, Ingo Keplinger, Tobias Siqueira, Gilberto De Freitas Vanneste, Felix Goury, Olivier Duriez, Christian Nanayakkara, Thrishantha Vanderborght, Bram Brancart, Joost Terryn, Seppe Rich, Steven I. Liu, Ruiyuan Fukuda, Kenjiro Someya, Takao Calisti, Marcello Laschi, Cecilia Sun, Wenguang Wang, Gang Wen, Li Baines, Robert Patiballa, Sree Kalyan Kramer-Bottiglio, Rebecca Rus, Daniela Fischer, Peer Simmel, Friedrich C Lendlein, Andreas Bioinspired Soft Robotics Laboratory Istituto Italiano di Tecnologia Italy Department of Industrial Engineering University of Florence Via di S. Marta 3 Florence50139 Italy Department of Mechanical Engineering Tokyo Institute of Technology Ookayama Meguro Tokyo152-8550 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56025 Italy Plant Biomechanics Group & Botanic Garden and Cluster of Excellence livMatS @ FIT University of Freiburg Germany Active and Intelligent Materials Laboratory Queen Mary University of London LondonE1 4NS United Kingdom Wood Materials Science Institute for Building Materials ETH Zurich Zurich8093 Switzerland Cellulose and Wood Materials Laboratory Empa Swiss Federal Laboratories for Materials Science and Technology Dübendorf8600 Switzerland University of Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Dyson School of Design Engineering Imperial College London United Kingdom Vrije Universiteit Brussel and imec Brussels Belgium Thin-Film Device Laboratory RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Center for Emergent Matter Science RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Electrical and Electronic Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan The BioRobotics Institute Scuola Superiore Sant’Anna Italy Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom Department of Mechanical Engineering National University of Singapore Singapore School of Mechanical Engineering and Automation Beihang University Beijing100191 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06511 United States CSAIL MIT Schwarzman College of Computing United States Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute of Physical Chemistry

Soft robotics aims at creating systems with improved performance of movement and adaptability in unknown, challenging, environments and with higher level of safety during interactions with humans. This Roadmap on Soft Robotics covers selected aspects for the design of soft robots significantly linked to the area of multifunctional materials, as these are considered a fundamental component in the design of soft robots for an improvement of their peculiar abilities, such as morphing, adaptivity and growth. The roadmap includes different approaches for components and systems design, bioinspired materials, methodologies for building soft robots, strategies for the implementation and control of their functionalities and behavior, and examples of soft-bodied systems showing abilities across different environments. For each covered topic, the author(s) describe the current status and research directions, current and future challenges, and perspective advances in science and technology to meet the challenges. © 2022 The Author(s). Published by IOP Publishing Ltd.

关键词： Machine design

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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