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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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Preface

Lecture Notes of the Institute for Computer Sciences, Social...

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Lecture Notes of the Institute for computer Sciences, Social-Informatics and Telecommunications engineering, LNICST 2020年 337卷 v-vi页

作者： Wang, Xiaofei Leung, Victor C. M. Li, Keqiu Zhang, Haijun Hu, Xiping Liu, Qiang College of Intelligence and Computing Tianjin University Tianjin Tianjin China School of Computer Science and Technology Tianjin University Tianjin Tianjin China Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada University of Science and Technology Beijing China National University of Defense Technology Changsha China

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Automated segmentation of lesions in ultrasound using semi-pixel-wise cycle generative adversarial nets

arXiv

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arXiv 2019年

作者： Xing, Jie Li, Zheren Wang, Biyuan Yu, Bingbin Zanjani, Farhad G. Zheng, Aiwen Duits, Remco Tan, Tao Zhejiang cancer hospital China Department of Bioengineering Imperial College London LondonSW7 2AZ United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600 MB Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Department of Computing Tokyo Institute of Technology Tokyo Japan Robotic Innovation Center German Research Center of Artificial Intelligence Bremen Germany

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Autonomic Management of 3D Cardiac Simulations 4

Autonomic Management of 3D Cardiac Simulations

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4th IEEE International Conference on Cloud and Autonomic computing, ICCAC 2017

作者： Esmaili, Ehsan Akoglu, Ali DItzler, Gregory Hariri, Salim Moukabary, Talal Szep, Jeno NSF Center for Cloud and Autonomic Computing Department of Electrical and Computer Engineering University of Arizona TucsonAZ United States

ISBN: (纸本)9781538619391

Large scale scientific applications in general and especially cardiac simulations experience different execution phases at runtime and each phase has different computational and communication requirements. An optimal solution or numerical scheme for one execution phase might not be appropriate for the next phase of the application execution. We propose an autonomic management framework, which is built on the physics aware programming (PAP) paradigm for accelerating the cardiac simulations further beyond what can be achieved through traditional parallelization efforts. This approach effectively exploits the physical properties of the cardiac simulation by being smart in the development of simulation algorithms. The cardiac simulation phase is periodically monitored and analyzed to identify its current execution phase. We apply machine learning techniques to detect the phase of the simulation during each time step of the 3D model of a human ventricular epicardial myocyte simulation. For each change in the simulation phase, we exploit the spatial and temporal attributes, dynamically change the resolution of the simulation, and select the numerical algorithms/solvers that optimize its performance without sacrificing the accuracy of the simulation. We compare the performance of the PAP-based algorithm in terms of simulation accuracy and execution time with respect to the reference simulation, which is considered the high-precision implementation. We achieve an overall speedup of 28.4× with a simulation accuracy of 99.9% with the PAP-based cardiac simulations. We also couple the PAP with a multi-graphics processing units (GPU) implementation, and show up to 191× speedup on a 16-GPU system. © 2017 IEEE.

关键词： Heart

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From FAIR to CURE: Guidelines for Computational Models of Biological Systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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MapReduce-based parallel GEP algorithm for efficient function mining in big data applications

MapReduce-based parallel GEP algorithm for efficient functio...

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作者： Liu, Yang Ma, Chenxiao Xu, Lixiong Shen, Xiaodong Li, Maozhen Li, Pengcheng School of Electrical Engineering and Information Sichuan University Chengdu610065 China Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University Shanghai200092 China State Grid Shanxi Electric Power Company Xi'an710048 China

Gene expression programming (GEP) algorithm is one of the most effective function mining algorithms in enabling the mathematical equation fitting for the input dataset. However, GEP algorithm encounters low efficiency issue in big data processing due to large overhead in its evolution when it handles the large-scale data. In order to solve the issue, this paper presents two parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithm aims at speeding up the large-scale classification. However, it is lack of ability to output the mined equation explicitly. Therefore, based on the further improvements of the first algorithm, the second parallelized GEP algorithm aims at mining the equation efficiently and also outputs the equation explicitly and directly. The experimental results show that both algorithms are effective for processing large volume of data. © 2017 John Wiley & Sons, Ltd.

关键词： MapReduce

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The distributed minimum spanning tree problem

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Bulletin of the European Association for Theoretical computer Science 2018年第125期2018卷

作者： Pandurangan, Gopal Robinson, Peter Scquizzato, Michele Department of Computer Science University of Houston HoustonTX United States Department of Computing and Software McMaster University Hamilton Canada School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This article surveys the distributed minimum spanning tree (MST) problem, a central and one of the most studied problems in distributed computing. In this problem, we are given a network, represented as a weighted graph G = (V, E), and the nodes in the network communicate by message passing via the edges of G with the goal of constructing an MST of G in a distributed fashion, i.e., each node should identify the MST edges incident to itself. This article summarizes the long line of research in designing efficient distributed algorithms and showing lower bounds for the distributed MST problem, including the most recent developments which have focused on algorithms that are simultaneously round-and message-optimal. © 2018, European Association for Theoretical computer Science. All rights reserved.

关键词： Message passing

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A Truncation Scheme for the Analytical Formula of via Barrel-Plate Capacitance

A Truncation Scheme for the Analytical Formula of via Barrel...

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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： Si-Ping Gao En-Xiao Liu Francesco De Paulis Antonio Orlandi Department of Electrical and Computer Engineering National University of Singapore Singapore Electronics and Photonics Department Institute of High Performance Computing Singapore DIIIE University of L'Aquila L'Aquila Italy

ISBN: (纸本)9781538612422

The analytical formula of the via barrel-plate capacitance has been widely used for the analyses of signal integrity, power integrity and electromagnetic compatibility of via-plate-pair structures by way of a physics-based via circuit model. This formula involves an infinite summation which must be truncated in real calculation. An improper truncation could result in underestimated or even unphysical values. This paper extends our previous study to handle the situation where the outer boundary is PEC or PMC. A proper truncation scheme is proposed to address the above truncation issues. Its effectiveness is demonstrated by numerical cases.

关键词： Integrated circuit modeling Capacitance Convergence Electromagnetic compatibility Signal integrity Analytical models Printed circuits

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Theoretical limits and scaling laws for electrokinetic molecular concentration via ion concentration polarization

arXiv

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arXiv 2018年

作者： Ouyang, Wei Li, Zirui Ye, Xinghui Han, Jongyoon Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute of Laser and Optoelectronic Intelligent Manufacturing College of Mechanical and Electrical Engineering Wenzhou University Wenzhou325035 China Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

We develop the first theoretical model for the analytical description of ion concentration polarization (ICP)-based electrokinetic molecular concentration, which had not been possible due to the extraordinary complexity of the system. We define the two separate limits for the enrichment factor achievable in a given system and derive the scaling laws for critical parameters, which are validated by numerical simulations and experiments. This work provides clear theoretical explanations on the diverse experimental behaviors previously observed yet unexplainable, while setting solid foundation for the engineering of ICP-based concentrators and other fluid-coupled electrokinetic systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Polarization

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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