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arXiv 2020年

作者： Zhang, Kai Danelljan, Martin Li, Yawei Timofte, Radu Liu, Jie Tang, Jie Wu, Gangshan Zhu, Yu He, Xiangyu Xu, Wenjie Li, Chenghua Leng, Cong Cheng, Jian Wu, Guangyang Wang, Wenyi Liu, Xiaohong Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao Luo, Xiaotong Chen, Liang Zhang, Jiangtao Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Li, Xiaochuan Lang, Zhiqiang Nie, Jiangtao Wei, Wei Zhang, Lei Muqeet, Abdul Hwang, Jiwon Yang, Subin Kang, JungHeum Bae, Sungho Kim, Yongwoo Chen, Liang Zhang, Jiangtao Luo, Xiaotong Qu, Yanyun Jeon, Geun-Woo Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Marty, Steven Marty, Eric Xiong, Dongliang Chen, Siang Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Wang, Haicheng Bhaskara, Vineeth Levinshtein, Alex Tsogkas, Stavros Jepson, Allan Kong, Xiangzhen Zhao, Tongtong Zhao, Shanshan Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Nan, Nan Liu, Shuai Cai, Jie Meng, Zibo Ding, Jiaming Ho, Chiu Man Wang, Xuehui Yan, Qiong Zhao, Yuzhi Chen, Long Zhang, Jiangtao Luo, Xiaotong Chen, Liang Qu, Yanyun Sun, Long Wang, Wenhao Liu, Zhenbing Lan, Rushi Umer, Rao Muhammad Micheloni, Christian Computer Vision Lab ETH Zurich Switzerland State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China MAICRO China University of Electronic Science and Technology of China China McMaster University Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Xiamen University China Indian Institute of Technology Madras India Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Northwestern Polytechnical University China Kyung Hee University Korea Republic of Sang Myung University Korea Republic of Xianmen University China Yonsei University Korea Republic of ETH Zurich Switzerland Zhejiang University China Qingdao Hi-image Technologies Co. Ltd [Hisense Visual Technology Co.Ltd. China Xidian University China Samsung AI Centre Toronto Canada Dalian Maritime University China China Everbright Bank Co. Ltd China College of Engineering Trivandrum India InnoPeak Technology Inc. United States SenseTime Research Hong Kong Sun Yat-sen University China City University of Hong Kong Hong Kong Guilin University of Electronic Technology Guilin541004 China University of Udine Italy

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Correction to: Automatic differentiation of Glaucoma visual field from non-glaucoma visual field using deep convolutional neural network

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BMC medical imaging 2019年第1期19卷 40页

作者： Fei Li Zhe Wang Guoxiang Qu Diping Song Ye Yuan Yang Xu Kai Gao Guangwei Luo Zegu Xiao Dennis S C Lam Hua Zhong Yu Qiao Xiulan Zhang Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-sen University Guangzhou China. SenseTime Group Limited Hong Kong China. Guangdong key lab of Computer Vision & Virtual Reality Multimedia Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. C-MER Dennis Lam Eye Hospital Shenzhen China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming China. zhoculist@***. Guangdong key lab of Computer Vision & Virtual Reality Multimedia Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. yu.qiao@***. Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-sen University Guangzhou China. zhangxl2@***.

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Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets 1

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Yu, Lequan Cheng, Jie-Zhi Dou, Qi Yang, Xin Chen, Hao Qin, Jing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Kowloon Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters. © Springer International Publishing AG 2017.

关键词： Medical applications

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Guo, Dejun Wang, Hesheng Chen, Weidong Liu, Ming Xia, Zeyang Leang, Kam K. Lab University of Utah Robotics Center Salt Lake CityUT84112 United States Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute of Biomedical and Health Engineering Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781509046331

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： Mobile robots

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A novel photometric stereo method with nonisotropic point light sources 23

A novel photometric stereo method with nonisotropic point li...

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23rd International Conference on Pattern Recognition, ICPR 2016

作者： Nie, Ying Zhan, Song Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781509048472

This paper presents a photometric stereo method with nonisotropic point light sources. Subject to the non-uniform lighting conditions produced by the nonisotropic point sources, each incident light ray should be precisely determined so as to realize an accurate calculation of surface normal. In the proposed method, radiance model of the light source is firstly introduced to the classical photometric stereo framework. By considering the distance and angular attenuations of incident light rays, a precise description for the lighting field can be established. Based on the initial 3D reconstruction result, an iterative process is introduced to optimize the primary light model parameters with respect to the unknown distance factor. The experimental setup is quite simple, which only consists of some LEDs and one camera. And the experimental results show that, with the proposed method, accuracy of the reconstructed surface normal can be greatly improved in comparison with some conventional light models. © 2016 IEEE.

关键词： Light sources

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Single shot text detector with regional attention

arXiv

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arXiv 2017年

作者： He, Pan Huang, Weilin He, Tong Zhu, Qile Qiao, Yu Li, Xiaolin National Science Foundation Center for Big Learning University of Florida Department of Engineering Science University of Oxford Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://***/. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

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Direction-aware spatial context features for shadow detection

arXiv

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arXiv 2017年

作者： Hu, Xiaowei Zhu, Lei Fu, Chi-Wing Qin, Jing Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Dejun Guo Hesheng Wang Weidong Chen Ming Liu Zeyang Xia Kam K. Leang DARC (Design Automation Robotics & Control) Lab University of Utah Robotics Center Salt Lake City Utah USA State Key Laboratory of Robotics and System (HIT) Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Institute of Biomedical and Health Engineering Chinese Academy of Sciences Shenzhen China

关键词： Cameras Robot vision systems Kinematics Mobile robots Calibration Automation

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Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

arXiv

引用

arXiv 2017年

作者： Yu, Lequan Cheng, Jie-Zhi Dou, Qi Yang, Xin Chen, Hao Qin, Jing Heng, Pheng-Ann Dept. of Computer Science and Engineering Chinese University of Hong Kong Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coeficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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