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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Viewpoint selection for photographing architectures

arXiv

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arXiv 2017年

作者： He, Jingwu Wang, Linbo Zhou, Wenzhe Zhang, Hongjie Cui, Xiufen Guo, Yanwen National Key Lab for Novel Software Technology Nanjing University NanJing210023 China MOE Key Laboratory of Intelligent Computing and Signal Processing Institute of Media Computing Anhui University HeFei230039 China Samsung Electronics R&D China

—This paper studies the problem of how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from professional photographs of world famous landmarks that are available on the Internet. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 15 world famous architectures as well as their 3D models from the Internet. Viewpoint recovery for images is carried out through an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users’ viewpoint preferences when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues and perform viewpoint recommendation by learning from both 2D and 3D features using a specifically designed SVM-2K multi-view learner, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. The experiments also demonstrate that our system can be used to recommend viewpoints for rendering textured 3D models of buildings for the use of architectural design, in addition to viewpoint evaluation of photographs and recommendation of viewpoints for photographing architectures in practice. Copyright © 2017, The Authors. All rights reserved.

关键词： Photography

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Viewpoint selection for taking a good photograph of architecture 24

Viewpoint selection for taking a good photograph of architec...

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24th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2016

作者： He, Jingwu Zhou, Wenzhe Wang, Linbo Zhang, Hongjie Guo, Yanwen National Key Lab for Novel Software Technology Nanjing University NanJing210023 China MOE Key Laboratory of Intelligent Computing and Signal Processing Institute of Media Computing Anhui University HeFei230039 China

ISBN: (纸本)9783038680246

This paper studies the problem of how to choose the viewpoint for taking good photographs for architecture. We achieve this by learning from professional photographs of world famous landmarks that are available in the Internet. Unlike the previous efforts devoted to photo quality assessment which mainly rely on visual features, we show in this paper combining visual features with geometric features computed on the 3D models can result in a more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 6 world famous architectures as well as their 3D models from Internet. Viewpoint recovery for images is carried out by an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users' viewpoint preference when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues, and perform viewpoint recommendation by learning from both 2D and 3D features, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. © 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.

关键词： Architecture

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A simple and efficient fusion framework for surveillance images

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu

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Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 2016年第6期 149-156页

作者： Chen, Lili Guo, Hongjun Laboratory of Intelligent Information Processing Suzhou University Suzhou China Key Laboratory of Intelligent Computing and Signal Processing of MOE Anhui University Hefei China

Purpose. Aiming at solving the fusion issue of surveillance images, a simple and efficient fusion framework using block compressed sensing sampling (BCSS) is proposed in this paper, which consists of two fusion methods using basic-BCSS and sliding-BCSS respectively. methodology. With the superiority of low sampling ratio and low computational complexity, compressed sensing (CS) theory is widely used in signal processing. The basic-BCSS is a basic version of block based CS, in which the source image is divided into distinct blocks, and the sliding-BCSS is a modified version of basic-BCSS proposed for the first time, in which the image is divided into small sliding blocks for each pixel with appropriate padding. The basic idea of the fusion framework is to select the blocks or pixels with greater L2-norm of the BCSS measurement outputs of the divided blocks in spatial domain. findings. The fusion framework is tested on three pairs of grayscale surveillance images, including infrared and visible images, and millimeter-wave and visible images, and compared with several traditional fusion methods. Experimental results demonstrate that the proposed fusion framework can significantly improve the fusion quality and speed simultaneously. Originality. A simple and efficient fusion framework using BCSS in spatial domain is proposed for the first time. Practical value. It has a certain practical meaning for real-time surveillance applications. © Lili Chen, Hongjun Guo, 2016.

关键词： Image fusion

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Multi-view Metric Learning for Multi-view Video Summarization

Multi-view Metric Learning for Multi-view Video Summarizatio...

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International Conference on Cyberworlds

作者： Linbo Wang Xianyong Fang Yanwen Guo Yanwei Fu MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Data Science Fudan University China

Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.

关键词： Extraterrestrial measurements Optimization Manifolds Visualization Minimization Eigenvalues and eigenfunctions

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

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Application of a multi-factor model in the grain yield prediction

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Metallurgical and Mining Industry 2015年第8期7卷 468-474页

作者： Chen, Lili Guo, Hongjun Laboratory of Intelligent Information Processing Suzhou University Suzhou Anhui China The Key Laboratory of Intelligent Computing and Signal Processing of MOE Anhui University Hefei Anhui China

A multi-factor model for the grain yield prediction based on the previous data and relevant impacting factors is reported. In this model, the weight coefficient of each individual factor that affected the grain yield historically is analyzed by the variation coefficient method and the conversion degree function in the attribute theory. The effects of various factors on the predicted grain yield are then used as standard vectors and subjected to a similarity-based search in the matrix of historical values. The predicted total grain yield is then determined by multiplying the sown area by the unit grain yield which is obtained by the highest similarity between the historical data and those predicted. Compared to the results obtained by the BP nerve network method, this method is simpler, more flexible, less time-consuming, and more accurate.

关键词： Forecasting

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A co-evolutionary particle swarm optimization with dynamic topology for solving multi-objective optimization problems

Advances in Modelling and Analysis A

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Advances in Modelling and Analysis A 2016年第1期53卷 145-159页

作者： Wu, Daqing Tang, Lixiang Li, Haiyan Ouyang, LiJun Computer Science and Technology Institute University of South China HangyangHunan China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200240 China Zigong643000 China Key Laboratory of Guangxi High Schools for Complex System and Computational Intelligence Guangxi University for Nationalities Nanning530006 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University HefeiAnhui Province230039 China Department of Business Administration Hunan University of Finance and Economics Hunan410205 China

This paper proposes a multi-objective with dynamic topology particle swarm optimization (PSO) algorithm for solving multi-objective problems, named DTPSO. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, DTPSO uses two dynamic local best particles to lead the search particles with multiple populations to deal with multiple objectives, and maintains diversity of new found non-dominated solutions via partitioned the searching space into fixed number of cells. The proposed DTPSO is validated through comparisons with other two multi-objective algorithms using established benchmarks and metrics. Simulation results demonstrated that DTPSO shows competitive, if not better, performance as compared to the other algorithms. © 2016, AMSE Press. All rights reserved.

关键词： Multiobjective optimization

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EPAS: Efficient privacy-preserving authentication scheme for VANETs-based emergency communication

Journal of Software

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Journal of Software 2013年第8期8卷 1914-1922页

作者： Jia, Xuedan Yuan, Xiaopeng Meng, Lixia Wang, Liangmin School of Computer Science and Telecommunication Engineering Jiangsu University Zhenjiang China School of Electronic Engineering and Optoelectronic Techniques NUST Nanjing China Key Laboratory of Intelligent Computing and Signal Processing MOE Anhui University Hefei China

Vehicular Ad Hoc Networks (VANETs) can provide participants with security services and entertainment information during the driving. To guarantee correct and smooth operations of VANETs, it is necessary to achieve efficient authentication with user privacy preserving. Current solutions either cannot satisfy privacy requirements, or are not efficient in message verification. Moreover, all the existing schemes are RSU-based. We, for the first time, apply VANETs to emergency communication during disaster rescue, and effective authentication scheme is proposed in accordance with the actual environment, where there is no fixed road-side unit (RSU). In this paper, we present an efficient identity based signature scheme EPAS, which satisfies conditional privacy requirements through software solution. In aspect of efficiency, both lightweight signature and batch verification are employed to provide effective authentication. Extensive theoretical and experimental analyses demonstrate the security and efficiency of EPAS in terms of privacy-preserving and low authentication delay. © 2013 ACADEMY PUBLISHER.

关键词： Vehicular ad hoc networks

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