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arXiv 2022年

作者： Kampfrath, Michelle Staritzbichler, René Hernández, Guillermo Pérez Rose, Alexander S. Tiemann, Johanna K.S. Scheuermann, Gerik Wiegreffe, Daniel Hildebrand, Peter W. Charité Universitätsmedizin Berlin Institute of Medical Physics and Biophysics Berlin Germany Leipzig University Image and Signal Processing Group Leipzig Germany University of Copenhagen Linderstrøm-Lang Centre for Protein Science Copenhagen Denmark Mol* Consortium

Molecular dynamics simulation is a proven technique for computing and visualizing the time-resolved motion of macromolecules at atomic resolution. The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers without requiring advanced skills, facilitating interactive exploration and collaborative visual analysis. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories and improve their online viewing and analysis. With the new instance, the MDsrv simplifies the creation of sessions, which allows the exchange of MD trajectories with preset representations and perspectives. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets, which greatly expands its applicability and use, as the data no longer needs to be accessible on a local server. In addition, initial analyses such as sequence or structure alignments, distance measurements, or RMSD calculations have been implemented, which optionally support visual analysis. Finally, based on Mol*, MDsrv now provides faster and more efficient visualization of even large trajectories compared to its predecessor tool NGL. © 2022, CC BY.

关键词： Trajectories

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Prediction of functional therapeutic response to thrombectomy in acute stroke using neural networks

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Clinical Neurophysiology 2024年 159卷 e5-e5页

作者： Stoll, K. von Braun, M. Kürsten, D. Welle, F. Wawrzyniak, M. Groos, A. Klingbeil, J. Stockert, A. Kaiser, D.P.O. Kellner, E. Reisert, M. Hoffmann, K. Scheuermann, G. Gillmann, C. Saur, D. Neuroimaging Laboratory Department of Neurology University of Leipzig Medical Center Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Leipzig University Leipzig Germany Institute of Neuroradiology University Medical Center Carl Gustav Carus Dresden Germany Medical Physics Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Freiburg Germany Institute of Neuroradiology University of Leipzig Medical Center Leipzig Germany Image and Signal Processing Group Institute for Informatics Leipzig University Leipzig Germany

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Overcoming thresholds – Utilizing convolutional neural networks for predicting individual thrombectomy response in acute ischemic stroke

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Clinical Neurophysiology 2024年 159卷 e8-e8页

作者： von Braun, M. Scholl, K. Peter, L. Welle, F. Schneider, H.R. Wawrzyniak, M. Kaiser, D.P.O. Henkelmann, J. Prasse, G. Kellner, E. Reisert, M. Klingbeil, J. Stockert, A. Lobsien, D. Hoffmann, K. Scheuermann, G. Gilmann, C. Saur, D. Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig) Leipzig University Germany Neuroimaging Laboratory Department of Neurology University of Leipzig Medical Center Germany Institute of Neuroradiology University Medical Center Carl Gustav Carus Dresden Germany Department of Radiology University of Leipzig Medical Center Germany Institute of Neuroradiology University of Leipzig Medical Center Germany Medical Physics Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Germany Institute for Diagnostic and Interventional Radiology and Neuroradiology Helios Hospital Erfurt Germany Image and Signal Processing Group Institute for Informatics Leipzig University Germany

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TH-289. Evaluation of ictal EEG source imaging with sliding window approach to localize the epileptogenic focus

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Clinical Neurophysiology 2022年 141卷 S180-S180页

作者： Baroumand, Amir G. Arbune, Anca Beniczky, Sandor van Mierlo, Pieter Medical Image and Signal Processing Group Ghent University Ghent Belgium Epilog NV Ghent Belgium Neurology Clinic Fundeni Clinical Institute Bucharest Romania Department of Clinical Neurophysiology Danish Epilepsy Centre Dianalund Denmark Department of Clinical Neurophysiology Aarhus University Hospital Aarhus Denmark Epoilog NV Ghent Belgium

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Automated Exudates Detection in Retinal Fundus image Using Morphological Operator and Entropy Maximization Thresholding

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Journal of Biomedical Science and Engineering 2019年第3期12卷 212-224页

作者： G. H. Kom B. C. Wouantsa Tindo J. R. Mboupda Pone A. B. Tiedeu Research Unit of Automation and Applied Informatics (URAIA) Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Research Group on Medical Technologies Applied to the Tropics (GRETMAT) Laboratory of Electronics and Signal Processing (LETS) ENSP University of Yaoundé I Yaoundé Cameroon Research Unit for Condensed Matter Electronics and Signal Processing (LAMACETS) Department of Physics University of Dschang Dschang Cameroon Signal Image and Systems Laboratory Ebolowa Cameroon

Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).

关键词： Diabetic Retinopathy Retinal Fundus image Exudates Entropy Maximization Thresholding

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Automated long-term EEG analysis to localize the epileptogenic zone

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Epilepsia Open 2017年第3期2卷 322-333页

作者： van Mierlo, Pieter Strobbe, Gregor Keereman, Vincent Birot, Gwénael Gadeyne, Stefanie Gschwind, Markus Carrette, Evelien Meurs, Alfred Van Roost, Dirk Vonck, Kristl Seeck, Margitta Vulliémoz, Serge Boon, Paul Medical Image and Signal Processing Group Department of Electronics and Information Systems Ghent University–iMinds Medical IT Department Ghent Belgium Functional Brain Mapping Laboratory Department of Fundamental Neurosciences University of Geneva Geneva Switzerland Laboratory for Clinical and Experimental Neurophysiology Neurobiology and Neuropsychology Department of Neurology Ghent University Hospital Ghent Belgium Epilepsy and EEG Unit University Hospital of Geneva Geneva Switzerland Department of Neurosurgery Ghent University Hospital Ghent Belgium

Objective: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. Methods: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. Results: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. Significance: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus. © 2017 The Authors. Epilepsia Open published by Wiley Periodicals Inc. on behalf of International League Against Epilepsy.

关键词： Automated spike detection Automated spike localization EEG source imaging Patient-specific head model

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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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Nature medicine 2024年第12期30卷 3779页

作者： Sebastian Moguilner Sandra Baez Hernan Hernandez Joaquín Migeot Agustina Legaz Raul Gonzalez-Gomez Francesca R Farina Pavel Prado Jhosmary Cuadros Enzo Tagliazucchi Florencia Altschuler Marcelo Adrián Maito María E Godoy Josephine Cruzat Pedro A Valdes-Sosa Francisco Lopera John Fredy Ochoa-Gómez Alfredis Gonzalez Hernandez Jasmin Bonilla-Santos Rodrigo A Gonzalez-Montealegre Renato Anghinah Luís E d'Almeida Manfrinati Sol Fittipaldi Vicente Medel Daniela Olivares Görsev G Yener Javier Escudero Claudio Babiloni Robert Whelan Bahar Güntekin Harun Yırıkoğulları Hernando Santamaria-Garcia Alberto Fernández Lucas David Huepe Gaetano Di Caterina Marcio Soto-Añari Agustina Birba Agustin Sainz-Ballesteros Carlos Coronel-Oliveros Amanuel Yigezu Eduar Herrera Daniel Abasolo Kerry Kilborn Nicolás Rubido Ruaridh A Clark Ruben Herzog Deniz Yerlikaya Kun Hu Mario A Parra Pablo Reyes Adolfo M García Diana L Matallana José Alberto Avila-Funes Andrea Slachevsky María I Behrens Nilton Custodio Juan F Cardona Pablo Barttfeld Ignacio L Brusco Martín A Bruno Ana L Sosa Ortiz Stefanie D Pina-Escudero Leonel T Takada Elisa Resende Katherine L Possin Maira Okada de Oliveira Alejandro Lopez-Valdes Brian Lawlor Ian H Robertson Kenneth S Kosik Claudia Duran-Aniotz Victor Valcour Jennifer S Yokoyama Bruce Miller Agustin Ibanez Latin American Brain Health Institute Universidad Adolfo Ibañez Santiago de Chile Chile. Cognitive Neuroscience Center Universidad de San Andrés Buenos Aires Argentina. Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA. Universidad de los Andes Bogota Colombia. Global Brain Health Institute (GBHI) University of California San Francisco CA USA. Global Brain Health Institute (GBHI) Trinity College Dublin Dublin Ireland. The University of California Santa Barbara (UCSB) Santa Barbara CA USA. Escuela de Fonoaudiología Universidad San Sebastián Santiago de Chile Chile. Grupo de Bioingeniería Decanato de Investigación Universidad Nacional Experimental del Táchira San Cristóbal Venezuela. Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile. University of Buenos Aires Buenos Aires Argentina. The Clinical Hospital of Chengdu Brain Sciences Institute University of Electronic Sciences and Technology of China Chengdu China. Technology of China Chengdu China. Cuban Neuroscience Center La Habana Cuba. Grupo de Neurociencias de Antioquia (GNA) University of Antioquia Medellín Colombia. Department of Psychology Master Program of Clinical Neuropsychology Universidad Surcolombiana Neiva Neiva Colombia. Department of Psychology Universidad Cooperativa de Colombia Arauca Colombia. Neurocognition and Psychophysiology Laboratory Universidad Surcolombiana Neiva Colombia. Reference Center of Behavioural Disturbances and Dementia School of Medicine University of Sao Paulo Sao Paulo Brazil. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center University of Sao Paulo Sao Paulo Brazil. Center for Social and Cognitive Neuroscience School of Psychology Universidad Adolfo Ibáñez Santiago Chile. Neuropsychology and Clinical Neuroscience Laboratory (LANNEC) Physiopathology Program-Institute of Biomedical Sciences (ICBM) Neuroscience and East Neuroscience Dep

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ID 178 – Longitudinal simultaneous DBS fMRI in the rodent brain

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Clinical Neurophysiology 2016年第3期127卷 e81-e81页

作者： N. Van Den Berge C. Vanhove B. Descamps I. Dauwe P. van Mierlo R. Raedt K. Vonck P. Boon R. Van Holen Medical Image and Signal Processing Group Ghent University-iMinds Medical IT department Ghent Belgium Laboratory for Clinical and Experimental Neurophysiology Neurobiology and Neuropsychology Ghent University Hospital Ghent Belgium

Objective The effects of Deep Brain Stimulation (DBS) have been studied primarily by electrophysiological and neurochemical studies, which lack the ability to elucidate DBS-related responses on a whole-brain scale. With this study our aim is to investigate DBS-induced global neuronal network activation in rats with functional Magnetic Resonance Imaging (fMRI). Methods Three times FMRI was done in seven rats, which were stereotactically implanted with a MR-compatible DBS-electrode in the right hippocampus . High frequency Poisson distributed stimulation was applied using a block-design paradigm. Response maps ( p < 0.05) were obtained with Independent Component Analysis . Results Our data indicate that real-time hippocampal DBS evokes a uni- or bilateral BOLD response in hippocampal and mesolimbic structures, depending on the applied stimulation intensity. Results were reproducible in time and in-between subjects. Conclusions We present that DBS-fMRI can be used to detect whole-brain responses to circuit activation with different stimulation intensities, making this technique potentially powerful for exploring DBS-induced cerebral changes for preclinical and clinical DBS. Key message A better understanding of the whole-brain effect of DBS is necessary to improve treatment efficacy in patients. Successful translation of this research to patients might reduce the number of non-responders to this invasive and expensive treatment.

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