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Recognition of temporal lobe epilepsy based on high throughput analysis of MRI images

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2016年 37卷 78-82页

作者： Lian, Yuxi Yu, Jinhua Wang, Yuanyuan Shi, Zhifeng Chen, Liang Department of Electronic Engineering Fudan University Shanghai200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200032 China Department of Neurosurgery Huashan Hospital Fudan University Shanghai200400 China

Temporal lobe epilepsy is a usual kind of epileptic *** recognition of temporal lobe epilepsyfrom MRI (magnetic resonance imaging) images can avoid the harm of radiation generated by PET (position emission computed tomography). An algorithm is proposed to recognize the temporal lobe epilepsybased onhigh throughput analysis of MRI ***, the brain MRI images are registered to the standardbrain atlas with SPM12 platform and the hippocampusregion is extracted as the ROI (Region of Interest) according to the AAL (anatomical automatic labeling) atlas in the same coordinate *** high throughput analysis is conducted to extract 540 image features from the ROI region. Finally, the Bagging method is used to distinguish normal persons and *** for 20 normal persons and 10 patients withtemporal lobe epilepsy are made to validate the proposed algorithm. The results indicate that the algorithm can obtain good classification performance with 100% accuracy. Hence, the proposed algorithm based on high throughout analysis and the Bagging classification method can be adopted to recognize temporal lobe epilepsy effectively. © 2016, Science Press. All right reserved.

关键词： Magnetic resonance imaging

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Bayesian data assimilation for estimating epidemic evolution: A COVID-19 study

arXiv

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arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

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Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

arXiv

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arXiv 2017年

作者： Jiang, Dongsheng Dou, Weiqiang Vosters, Luc Xu, Xiayu Sun, Yue Tan, Tao Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention Shanghai China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Xian Jiaotong University Xian710049 China Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xian Jiaotong Universty Xian710049 China Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands ScreenPoint Medical Nijmegen Netherlands

The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level parameter. Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. The advantage of the proposed methodology is that the learning based model can be directly used in the denosing process without optimization and even without the noise level parameter. Specifically, a ten convolutional layers neural network combined with residual learning and multi-channel strategy was proposed. Two training ways: training on a specific noise level and training on a general level were conducted to demonstrate the capability of our methods. Experimental results over synthetic and real 3D MR data demonstrate our proposed network can achieve superior performance compared with other methods in term of both of the peak signal to noise ratio and the global of structure similarity index. Without noise level parameter, our general noise-applicable model is also better than the other compared methods in two datasets. Furthermore, our training model show good general applicability. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Super Resolution of Pathology images with Hierarchical Feature Integration and Local image Patterns

SSRN

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SSRN 2024年

作者： Gao, Chaofei Xu, Feng Li, Lei Wang, Shuyang Ling, Ren Zhang, Xie Deng, Xi Zhou, Mengzhe Ling, Jin Department of Gastroenterology The Affiliated Lihuili Hospital of Ningbo University Zhejiang Ningbo315040 China Pathological Diagnosis Department Meikang Medicalsystem Laboratory. Ningbo Zhejiang 315105 China Department of Pathology School of Basic Medical Sciences Fudan University Xuhui District Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Xuhui District Shanghai200032 China Department of Research and Development Shanghai Laizi Software Technology Co. LTD. Fengxian District Shanghai201499 China Ningbo Clinical Pathology Diagnosis Center Zhejiang Ningbo315020 China MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRist Department of Automation Tsinghua University Beijing100084 China

The rapid development of artificial intelligence and digital pathology has demonstrated promise in precision diagnosis in clinical. However, pathology analysis at single cell and sub-cellular level is still limited by the image resolution as the high resolution image acquirement is expensive and time-consuming. Additionally, most super resolution models ignored the intrinsic characteristics of pathology images without reaching satisfactory performance. In this paper, we developed a hierarchical super resolution deep learning framework based on local image patterns (HLIP) to reconstruct super resolution images from the low resolution ones with arbitrary input size and magnification. HLIP integrates hierarchical feature maps and conducts reconstruction by considering the local image patterns of neighboring pixels. Benchmark analysis shows HLIP achieves the best performance on both the internal and external validation datasets and the generated super resolution images approximate high resolution ones, which have reached clinical quality. It is revealed that HLIP can be applied in multiple clinical scenes as a versatile tool to improve the performance of other models on the tasks including the gland segmentation, cell segmentation and Helicobacter pylori detection. Moreover, HLIP can be integrated directly into the existing computer-aided pathology diagnostic systems for real-time super resolution. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Algorithm for abdominal surgery incision detection based on automated 3D ultrasound coronal plane images

引用

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2016年 37卷 23-27页

作者： Wu, Jun Wen, Le Wang, Yuanyuan Zhang, Yufeng Chen, Yue Chen, Lin Department of Electronic Engineering Yunnan University Kunming650504 China Department of Electronic Engineering Fudan University Shanghai200433 China Key Lab of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200032 China Department of Ultrasound Huadong Hospital Fudan University Shanghai200040 China

Incisional hernia is an abdominal wall defect that occurs in the original incision area. Accurate positioning of surgery incision has important clinical value for incisional hernia diagnosis. In this paper, an algorithm is proposed for abdominal surgery incision detection based on automated 3D ultrasound coronal plane images. Firstly, the coronal plane ultrasound images are filtered by using Speckle Reducing Anisotropic Diffusion method. Then, the image pre-processing operations are used to reduce the amount of calculation and improve the execution efficiency. Finally, a straight-line equation of the original surgery incision is obtained by using edge detection, maximum distance screening and linear fitting. It is shown in the results that the fitting line of the surgery incision is very close to the location of the actual surgery incision, which means the algorithm has satisfied fitting features and high detection precision. © 2016, Science Press. All right reserved.

关键词： Surgery

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

引用

BMC medical IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Catalpol ameliorates depressive-like behaviors in CUMS mice via oxidative stress-mediated NLRP3 inflammasome and neuroinflammation

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解剖学杂志 2021年第S1期44卷 125-125页

作者： Wu Haoran Wang Yalim Hu Juntao Department of Anatomy Histology and EmbryologyDepartment of Integrative Medicine and NeurobiologySchool of Basic Medical SciencesInstitutes of Brain ScienceState Key Laboratory of Medical Neurobiology and Collaborative Innovation Center for Brain ScienceShanghai Medical CollegeFudan UniversityKey Laboratory of Medical Imaging Computing and Computer Assisted Intervention of ShanghaiShanghai 200032China

The purpose of the present study was to investigate whether catalpol exhibited neuro-protective effects in chronic unpredictable mild stress(CUMS)mice through oxidative stress-mediated nucleotide-binding oligomerizati... 详细信息

关键词： NLRP3 inflammation protective

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Author Correction: Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

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Nature communications 2021年第1期12卷 4370页

作者： Xueyi Zheng Zhao Yao Yini Huang Yanyan Yu Yun Wang Yubo Liu Rushuang Mao Fei Li Yang Xiao Yuanyuan Wang Yixin Hu Jinhua Yu Jianhua Zhou Department of Ultrasound Sun Yat-Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. Department of Electronic Engineering Fudan University Shanghai China. Paul C. Lauterbur Research Center for Biomedical Imaging Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. The key laboratory of medical imaging computing and computer assisted intervention of Shanghai Shanghai China. Department of Electronic Engineering Fudan University Shanghai China. jhyu@***. The key laboratory of medical imaging computing and computer assisted intervention of Shanghai Shanghai China. jhyu@***. Department of Ultrasound Sun Yat-Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. zhoujh@***.

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