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arXiv 2021年

作者： Wang, Kexin Wang, Shuo Xiong, Minghua Wang, Chengyan Wang, He Department of Physics Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai China Shanghai Zhiyu Software Information Co. Ltd China Human Phenome Institute Fudan University Shanghai China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China

Clinically significant portal hypertension (CSPH) is a severe complication of chronic liver disease associated with cirrhosis, which is diagnosed by the measurement of hepatic venous pressure gradient (HVPG). However, HVPG measurement is invasive and therefore difficult to be widely applied in clinical routines. There is no currently available technique to measure HVPG noninvasively. Computational fluid dynamics (CFD) has been used for noninvasive measurement of vascular pressure gradient in the intracranial and coronary arteries. However, it has been scarcely employed in the hepatic vessel system due to the difficulties in reconstructing precise vascular anatomies and setting appropriate boundary conditions. Several computer tomography and ultrasound based studies have verified the effectiveness of virtual HVPG (vHVPG) by directly connecting the portal veins and hepatic veins before CFD simulations [16,12]. We apply the latest techniques of phase-contrast magnetic resonance imaging (PC-MRI) and DIXON to obtain the velocity and vessel anatomies at the same time. Besides, we improve the CFD pipeline in regards to the construction of vessel connections and reduction of calculation time. The proposed method shows high accuracy in the CSPH diagnosis in a study containing ten healthy volunteers and five patients. The MRI-based noninvasive HVPG measurement is promising in the clinical application of CSPH diagnosis. © 2021, CC BY.

关键词： Computational fluid dynamics

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Equilibrium and transient thermodynamics: A unified dissipaton–space approach

arXiv

引用

arXiv 2020年

作者： Gong, Hong Wang, Yao Zhang, Hou-Dao Qiao, Qin Xu, Rui-Xue Zheng, Xiao Yan, YiJing Department of Chemical Physics University of Science and Technology of China HefeiAnhui230026 China Hefei National Laboratory for Physical Sciences at the Microscale and iChEM Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Digital Medical Research Center of School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

This work presents a unified dissipaton–equation–of–motion (DEOM) theory and its evaluations on the Helmholtz free energy change due to the isotherm mixing of two isolated subsystems. One is a local impurity and another is a nonlocal Gaussian bath. DEOM constitutes a fundamental theory for such open quantum mixtures. To complete the theory, we construct also the imaginary–time DEOM formalism via an analytical continuation of dissipaton algebra, which would be limited to equilibrium thermodynamics. On the other hand, the real–time DEOM deals with both equilibrium structural and nonequilibrium dynamic properties. Its combination with the thermodynamic integral formalism would be a viable and accurate means to both equilibrium and transient thermodynamics. As illustrations, we report the numerical results on a spin–boson system, with elaborations on the underlying anharmonic features, the thermodynamic entropy versus the von Neumann entropy, and an indication of "solvent–cage" formation. Beside the required asymptotic equilibrium properties, the proposed transient thermodynamics also supports the basic spontaneity criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Free energy

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Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network

Resting-state functional connectivity predicts individual la...

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第二十二届全国心理学学术会议

作者： Binke Yuan Nan Zhang Jing Yan Junfeng Lu Jinsong Wu Center for Language and Brain Shenzhen Institute of Neuroscience Department of Neurosurgery The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of China Glioma Surgery Division Department of NeurosurgeryHuashan HospitalFudan University Department of MRI The First Affiliated Hospital of Zhengzhou University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

Aim: Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. However, no study has attempted to investigate the language network basis at the whole brain level to determine the language network reorganization differences between low-grade glioma(LGG) and high-grade glioma(HGG) patients. Methods: Inclusion Criteria:(1) Pathologically confirmed glioma in left cerebral hemisphere and overlapped or was within the language network areas proposed by Fedorenko et al.(2010);(2) patients' age was 18–75 years old;(3) Right-handedness;(4) No symptoms of motor impairment;(5) No history of brain surgery;(6) No midline shift in structural images;(7) No history of other major neurological or psychiatric disorders. In total, 126 patients,including 77 patients with LGG and 49 patients with HGG met all inclusion criteria and completed the entire preoperative clinical, language, and imaging data acquisition. All processes strictly followed the requirements of the Declaration of Helsinki. This study was approved and supervised by the Ethics Committee of Huashan Hospital and the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, respectively. Written informed consents were obtained from the human subjects and their legal guardians. Language function: ABC(Aphasia Battery for Chinese speakers) was used and Aphasia Quotient(AQ) score(range, 0–100) was calculated for each patient. Rs-f MRI data: TR = 2,000 ms;TE = 35 ms;FA =90°;240 volumes;eyes closed. T1 and T2 images were also acquired for tumor mapping and registration. Rs-f MRI data preprocessing: slice timing, motion correction, normalization by DARTEL(MNI), smoothness(6 mm), detrend, nuisance regression, filter(0.01-0.1 Hz) and scrubbing(FD > 0.05). No patients exhibited a head motion > 3 mm maximum translation or 3° rotation. Nuisance signals included 36 head motion parameters, global signals, cerebrospinal

关键词： low-grade glioma high-grade glioma resting-state fMRI language machine learning

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

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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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Transformers in medical Image Analysis: A Review

arXiv

引用

arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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