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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： image segmentation

A Novel Representation for Video-based Person Reidentification with Attribute-constraints

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A Novel Representation for Video-based Person Reidentificati...

International Conference on Signal processing Proceedings (ICSP)

作者： Wanru Song Jieying Zheng Yahong Wu Qingqing Zhao Changhong Chen Feng Liu Jiangsu Key Lab of Image Processing and Image Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538646748;9781538646731

Person re-identification is an important task in the field of intelligent video surveillance, which has become one of the research focus spots in the field of computer vision. Video-based person re-identification aims to verify a pedestrian identity of the video sequences which captured from non-overlapping cameras at different time. In this paper, we propose a novel feature extractor based on LSTM networks. These LSTM networks are used to extract the effective space-time feature representation named the attribute-constraints space-time feature (ASTF). Different from other methods, we manually annotate pedestrians in videos with three attributes. In the meantime, the attributes with the IDs of pedestrians are regarded as labels to train the feature extractor. The ASTF representation for a testing video is extracted by this feature extractor, which is an effective space-time feature representation for video-based re-identification. Extensive experiments on two public datasets demonstrate that our approach outperforms the state-of-the-art video-based re-identification methods.

关键词： Feature extraction Task analysis Training Cameras Testing Measurement Video sequences

The statistic modeling of eye movement viewing S3D images 14th

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The statistic modeling of eye movement viewing S3D images

14th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2017

作者： Zhang, Chi Zhou, Jun Zhu, Shoucheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China Shanghai Yanan High School Shanghai200336 China

ISBN: (纸本)9789811081071

Nowadays, more and more families are willing to buy 3D TV to improve their watching experience. Stereo perception produced by watching 3D images or videos brings strong immersive watching experience to users. However, accumulated vision fatigue confuses users a lot after watching 3D TV for a long time. When watching 3D images, controlled by past recognition experience and visual attention mechanism, gaze point of two eyes is changing among different objects which have different depth of field. The eye movement in this changing process is called vergence. Vergence can be defined as movement of our eyes in opposite directions to locate the area of interest on the fovea and accommodation as alteration of the lens to obtain and maintain the area of interest focused on the fovea. So the more frequently the vergence process occurs, the more uncomfortable we feel. We expect to obtain several eye movement patterns, which can be considered as some typical visual attention patterns, by building a top-down recognition and visual attention model and then applying some clustering methods to find them. So we use an eye tracker to record eye movement data and then model it as a bayesian network model. The generative model is based on beta process and we build an Autoregression-HMM model to describe the relationship between latent eye movement patterns and eye movement data. To uncover parameters which represent different eye movement patterns in this model, we use MCMC method to calculate them with iterative computations. In this work, some different latent patterns existed in the sequential eye movement data can be revealed. After analyzing these patterns, we are able to find out some similarities and differences of visual attention models between different people watching the same image or between different images viewed by the same one. These conclusions can help to improve quality of 3D image thus lessening the users’ vision fatigue when watching 3D TV. This work also will con

关键词： Eye movements

Remote Sensing image Scene Classification Based on SURF Feature and Deep Learning

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Remote Sensing Image Scene Classification Based on SURF Feat...

Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Jinxiang Liang Jianwu Dang Yangping Wang Jingyu Yang Zhenhai Zhang Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing Gansu Provincial Key Lab of System Dynamics and Reliability of Rail Transport Equipment Lanzhou Bocai Technology Co. Ltd.

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Remote sensing image scene classification is one of the key points in remote sensing image interpretation. The traditional remote sensing image scene classification feature performance is not strong, and the deep learning extraction semantic feature process is complex. This paper proposes a fusion feature remote sensing image scene classification method which is based on artificial features and deep learning semantic features. Firstly, the SURF feature of the remote sensing image is extracted and encoded by the VLAD algorithm. The semantic feature of a remote sensing image is extracted by transfer learning. Then the feature reduction is performed by PCA algorithm and feature fusion is performed. Finally, the scene classifier is trained by using the random forest algorithm. The experimental results show that the classification accuracy and Kappa coefficient of this method are higher and the method is effective.

关键词： Feature extraction Remote sensing Machine learning Semantics image analysis Classification algorithms Forestry

Study on the Segmentation Method of Multi-Phase CT Liver Tumor Based on Dual Channel U-Nets

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Changchang Xu Dong Hu YaoDong Zhang Yuwei Pang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China The first clinical medical college of Nanjing Medical University Nanjing University of Posts and Telecommunications Nanjing 210003 China

As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.

关键词：

Depth-aware video frame interpolation

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arXiv 2019年

作者： Bao, Wenbo Lai, Wei-Sheng Ma, Chao Zhang, Xiaoyun Gao, Zhiyong Yang, Ming-Hsuan Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University University of California Merced United States Google

Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

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arXiv 2020年

作者： Fan, Zhun Li, Chong Chen, Ying Wei, Jiahong Loprencipe, Giuseppe Chen, Xiaopeng Di Mascio, Paola Key Lab of Digital Signal and Image Processing of Guangdong Province Department of Electronic and information Engineering College of Engineering Shantou University Shan’tou515063 China Department of Electronic and information Engineering College of Engineering Shantou University Shan’tou515063 China Department of Civil Constructional and Environmental Engineering Sapienza University of Rome Rome00184 Italy Department of Industrial Engineering Pusan National University Busan609735 Korea Republic of

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can

关键词： Convolution

Direct parametric reconstruction in dynamic PET using deep image prior and a novel parameter magnification strategy

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Computers in Biology and Medicine 2025年 194卷 110487页

作者： Xiaotong Hong Fanghu Wang Hao Sun Hossein Arabi Lijun Lu School of Biomedical Engineering Southern Medical University 1023 Shatai Road Guangzhou 510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University 1023 Shatai Road Guangzhou 510515 China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University 1023 Shatai Road Guangzhou 510515 China The WeiLun PET Center Department of Nuclear Medicine Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences 510080 Guangzhou China Division of Nuclear Medicine and Molecular Imaging Department of Medical Imaging Geneva University Hospital CH.1211 Geneva 4 Switzerland Pazhou Lab Guangzhou 510330 China

Background/Purpose Multiple parametric imaging in positron emission tomography (PET) is challenging due to the noisy dynamic data and the complex mapping to kinetic parameters. Although methods like direct parametric reconstruction have been proposed to improve the image quality, limitations persist, particularly for nonlinear and small-value micro-parameters (e.g., k 2 , k 3 ). This study presents a novel unsupervised deep learning approach to reconstruct and improve the quality of these micro-parameters. Methods We proposed a direct parametric image reconstruction model, DIP-PM, integrating deep image prior (DIP) with a parameter magnification (PM) strategy. The model employs a U-Net generator to predict multiple parametric images using a CT image prior, with each output channel subsequently magnified by a factor to adjust the intensity. The model was optimized with a log-likelihood loss computed between the measured projection data and forward projected data. Two tracer datasets were simulated for evaluation: 82 Rb data using the 1-tissue compartment (1 TC) model and 18 F-FDG data using the 2-tissue compartment (2 TC) model, with 10-fold magnification applied to the 1 TC k 2 and the 2 TC k 3 , respectively. DIP-PM was compared to the indirect method, direct algorithm (OTEM) and the DIP method without parameter magnification (DIP-only). Performance was assessed on phantom data using peak signal-to-noise ratio (PSNR), normalized root mean square error (NRMSE) and structural similarity index (SSIM), as well as on real 18 F-FDG scan from a male subject. Results For the 1 TC model, OTEM performed well in K 1 reconstruction, but both indirect and OTEM methods showed high noise and poor performance in k 2 . The DIP-only method suppressed noise in k 2 , but failed to reconstruct fine structures in the myocardium. DIP-PM outperformed other methods with well-preserved detailed structures, particularly in k 2 , achieving the best metrics (PSNR: 19.00, NRMSE: 0.3002, SSIM: 0

关键词： Compartmental model Deep image prior Dynamic PET Multiparametric imaging Parameter magnification

Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

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arXiv 2019年

作者： Liu, Zihan Meng, Lubin Zhang, Xiao Fang, Weili Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Design and Environment National University of Singapore 117566 Singapore Zhejiang Lab Hangzhou311121 China

Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography