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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

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A Mixed-attention Network for Automated Interventricular Septum Segmentation in Bright-blood Myocardial T2* MRI Relaxometry in Thalassemia

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Academic radiology 2025年

作者： Xiaofeng Wu Hangyu Wang Zeluan Chen Songsong Sun Zifeng Lian Xinyuan Zhang Peng Peng Yanqiu Feng School of Biomedical Engineering Southern Medical University Guangzhou 510000 China (X.W. H.W. Z.C. S.S. Z.L. X.Z. Y.F.) Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou 510000 China (X.W. H.W. Z.C. S.S. Z.L. X.Z. Y.F.) Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou 510000 China (X.W. H.W. Z.C. S.S. Z.L. X.Z. Y.F.). Department of Radiology The First Affiliated Hospital of Guangxi Medical University Nanning 530021 China (P.P.) NHC Key Laboratory of Thalassemia Medicine and Guangxi Key laboratory of Thalassemia Research Nanning Guangxi China (P.P.). Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou 510000 China (X.W. H.W. Z.C. S.S. Z.L. X.Z. Y.F.) Department of Radiology Shunde Hospital Southern Medical University (The First People's Hospital of Shunde Foshan) Foshan China (Y.F.). Electronic address: foree@***.

RATIONALE AND OBJECTIVES:This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia. MATERIALS AND METHODS:This retrospective study used multiple-gradient-echo cardiac MR scans from 419 thalassemia patients to develop and evaluate the segmentation network. The network was trained on 1.5 T images from Center 1 and evaluated on 3.0 T unseen images from Center 1, all data from Center 2, and the CHMMOTv1 dataset. Model performance was assessed using five metrics, and T2* values were obtained by fitting the network output. Bland-Altman analysis, coefficient of variation (CoV), and regression analysis were used to evaluate the consistency between automatic and manual methods. RESULTS:MA-BBIsegNet achieved a Dice of 0.90 on the internal test set, 0.85 on the external test set, and 0.81 on the CHMMOTv1 dataset. Bland-Altman analysis showed mean differences of 0.08 (95% LoA: -2.79 ∼ 2.63) ms (internal), 0.29 (95% LoA: -4.12 ∼ 3.54) ms (external) and 0.19 (95% LoA: -3.50 ∼ 3.88) ms (CHMMOTv1), with CoV of 8.9%, 6.8%, and 9.3%. Regression analysis yielded r values of 0.98 for the internal and CHMMOTv1 datasets, and 0.99 for the external dataset (p < 0.05). CONCLUSION:The IS segmentation network based on multiple-gradient-echo bright-blood images yielded T2* values that were in strong agreement with manual measurements, highlighting its potential for the efficient, non-invasive monitoring of myocardial iron deposition in patients with thalassemia.

关键词： Automatic segmentation Bright-blood myocardial images Magnetic resonance imaging Myocardial iron overload T2* measurement

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images

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Medical image analysis 2025年 104卷 103633页

作者： Yuli Chen Jiayang Bai Jinjie Wang Guoping Chen Xinxin Zhang Duan-Bo Shi Xiujuan Lei Peng Gao Cheng Lu College of Computer Science Shaanxi Normal University Xi'an 710119 China. Department of Pathology Qilu Hospital Shandong University Jinan 250012 China. College of Computer Science Shaanxi Normal University Xi'an 710119 China. Electronic address: xjlei@***. Department of Pathology Qilu Hospital Shandong University Jinan 250012 China Key Laboratory for Experimental Teratology of Ministry of Education Department of Pathology School of Basic Medical Sciences Shandong University Jinan 250012 China. Electronic address: gaopeng@***. Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Medical Research Institute Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China. Electronic address: lucheng@***.

Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its heterogeneous nature and the need to harness the complementary information from multiple nuclei sources in histopathology images. To tackle this critical problem, we introduce a novel multi-source hybrid feature fusion network named MSFusion. This network incorporates two types of hybrid features: deep learning features extracted by a novel Swin Transformer-based multi-branch network called MSwinT, and traditional handcrafted features that capture the morphological characteristics of multi-source nuclei. The primary branch of MSwinT captures the overall characteristics of the original images, while multiple auxiliary branches focus on identifying morphological features from diverse sources of nuclei, including tumor, mitotic, tubular, and epithelial nuclei. At each of the four stages for the branches in MSwinT, a functional KDC (key diagnostic components) fusion block with channel and spatial attentions is proposed to integrate the features extracted by all the branches. Ultimately, we synthesize the multi-source hybrid deep learning features and handcrafted features to improve the accuracy of IBC diagnosis and grading. Our multi-branch MSFusion network is rigorously evaluated on three distinct datasets, including two private clinical datasets (Qilu dataset and QDUH&SHSU dataset) as well as a publicly available Databiox dataset. The experimental results consistently demonstrate that our proposed MSFusion model outperforms the state-of-the-art methods. Specifically, the AUC for the Qilu dataset and QDUH&SHSU dataset are 81.3% and 90.2%, respectively, while the public Databiox dataset yields an AUC of 82.1%.

关键词： Handcrafted features Invasive breast cancer (IBC) grading Multi-branch deep learning network Multi-source hybrid feature fusion

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Multi-task deep learning with dynamic programming for embryo early development stage classification from time-lapse videos

arXiv

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arXiv 2019年

作者： Liu, Zihan Huang, Bo Cui, Yuqi Xu, Yifan Zhang, Bo Zhu, Lixia Wang, Yang Jin, Lei Wu, Dongrui Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Reproductive Medicine Center Huazhong University of Science and Technology Tongji Hospital Wuhan430074 China

Time-lapse is a technology used to record the development of embryos during in-vitro fertilization (IVF). Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF. This paper proposes a multi-task deep learning with dynamic programming (MTDL-DP) approach for this purpose. It first uses MTDL to pre-classify each frame in the time-lapse video to an embryo development stage, and then DP to optimize the stage sequence so that the stage number is monotonically non-decreasing, which usually holds in practice. Different MTDL frameworks, e.g., oneto-many, many-to-one, and many-to-many, are investigated. It is shown that the one-to-many MTDL framework achieved the best compromise between performance and computational cost. To our knowledge, this is the first study that applies MTDL to embryo early development stage classification from time-lapse videos. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

arXiv

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arXiv 2022年

作者： Chen, Zhifeng Liao, Congyu Cao, Xiaozhi Poser, Benedikt A. Xu, Zhongbiao Lo, Wei-Ching Wen, Manyi Cho, Jaejin Tian, Qiyuan Wang, Yaohui Feng, Yanqiu Xia, Ling Chen, Wufan Liu, Feng Bilgic, Berkin School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Radiology Stanford University Stanford CA United States Maastricht Brain Imaging Center Faculty of Psychology and Neuroscience University of Maastricht Netherlands Department of Radiotherapy Cancer Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Science Guangzhou China Siemens Medical Solutions BostonMA United States Department of Chemical Pathology The Chinese University of Hong Kong Hong Kong Division of Superconducting Magnet Science and Technology Institute of Electrical Engineering Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1×1.1×1.0 mm3 resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Correction: Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer

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Journal of translational medicine 2025年第1期23卷 140页

作者： Jing Yang Huifen Ye Xinjuan Fan Yajun Li Xiaomei Wu Minning Zhao Qingru Hu Yunrui Ye Lin Wu Zhenhui Li Xueli Zhang Changhong Liang Yingyi Wang Yao Xu Qian Li Su Yao Dingyun You Ke Zhao Zaiyi Liu Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangzhou China. Department of Cardiology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China. Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences 106 Zhongshan Er Road Guangzhou 510080 China. The Second School of Clinical Medicine Southern Medical University Guangzhou China. Department of Pathology The Sixth Affiliated Hospital of Sun Yat-Sen University Guangzhou China. Department of Radiology The Sixth Affiliated Hospital of Sun Yat-Sen University Guangzhou China. Department of Pathology The Third Affiliated Hospital of Kunming Medical University Yunnan Cancer Hospital Yunnan Cancer Center Kunming China. Department of Radiology The Third Affiliated Hospital of Kunming Medical University Yunnan Cancer Hospital Yunnan Cancer Center Kunming China. Department of Ophthalmology Guangdong Eye Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China. Department of Radiology Zhuhai People's Hospital Zhuhai Hospital Affiliated With Jinan University Zhuhai China. School of Medicine South China University of Technology Guangzhou China. Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences 106 Zhongshan Er Road Guangzhou 510080 China. ysjulien@***. School of Public Health Kunming Medical University 191 West Renmin Road Kunming 650500 China. youdingyun@***. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangzhou China. zhaoke@***. Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences 106 Zhongshan Er Road Guangzhou 510080 China. zhaoke@***. Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital Guangdo

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Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

arXiv

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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

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Recommendations on Designing Practical Interval Type-2 Fuzzy Systems

arXiv

引用

arXiv 2019年

作者： Wu, Dongrui Mendel, Jerry M. 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 Ming Hsieh Department of Electrical Engineering University of Southern California Los AngelesCA United States College of Artificial Intelligence Tianjin Normal University Tianjin China

Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system is more complex than that of its type-1 counterpart. There are many questions to be answered in designing an IT2 fuzzy system: Should singleton or non-singleton fuzzifier be used? How many membership functions (MFs) should be used for each input? Should Gaussian or piecewise linear MFs be used? Should Mamdani or Takagi-Sugeno-Kang (TSK) inference be used? Should minimum or product t-norm be used? Should type-reduction be used or not? How to optimize the IT2 fuzzy system? These questions may look overwhelming and confusing to IT2 beginners. In this paper we recommend some representative starting choices for an IT2 fuzzy system design, which hopefully will make IT2 fuzzy systems more accessible to IT2 fuzzy system designers. Copyright © 2019, The Authors. All rights reserved.

关键词： Membership functions

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Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers

arXiv

引用

arXiv 2020年

作者： Zhang, Xiao Wu, Dongrui Ding, Lieyun Luo, Hanbin Lin, Chin-Teng Jung, Tzyy-Ping Chavarriaga, Ricardo Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil Engineering and Mechanics Huazhong University of Science and Technology Wuhan430074 China Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia La Jolla CA92093 United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA92093 United States ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g., amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable;however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e., they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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