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Epidural electrical stimulation facilitates motor recovery in spinal cord injury involving the conus medullaris: A case study

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Med 2025年 100706页

作者： Albano, Luigi Emedoli, Daniele Agnesi, Filippo Romeni, Simone Losanno, Elena Toni, Laura Fossati, Veronica Ciucci, Chiara Gasperotti, Filippo Cociani, Leonardo Zucco, Giovanni Pompeo, Edoardo Mura, Cinzia Carpaneto, Jacopo Tettamanti, Andrea Castelnovo, Veronica Padul, Jeffrey David Mandelli, Carlo Barzaghi, Lina Raffaella Alemanno, Federica Caravati, Heike Butera, Carla Del Carro, Ubaldo Castellano, Antonella Falini, Andrea Agosta, Federica Filippi, Massimo Iannaccone, Sandro Mortini, Pietro Micera, Silvestro Modular Implantable Neuroprostheses (MINE) Laboratory Università Vita-Salute San Raffaele & Scuola Superiore Sant'Anna Milan Italy Neurosurgery and Gamma Knife Radiosurgery Unit IRCCS Ospedale San Raffaele Milan Italy Neuroimaging Research Unit Division of Neuroscience IRCCS Ospedale San Raffaele Milan Italy Department of Rehabilitation and Functional Recovery IRCCS Ospedale San Raffaele Milan Italy The BioRobotics Institute Health Science Interdisciplinary Research Center Department of Excellence Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Translational Neural Engineering Laboratory Neuro-X Institute Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland Neurophysiology Service IRCCS Ospedale San Raffaele Milan Italy Neuroradiology Unit and Cermac IRCCS Ospedale San Raffaele Milan Italy Università Vita-Salute San Raffaele Milan Italy Neurology Unit IRCCS Ospedale San Raffaele Milan Italy

Background: Emerging research increasingly supports that epidural spinal cord electrical stimulation (EES) combined with neurorehabilitation can improve motor recovery in spinal cord injury (SCI) subjects. Patients with lesions involving the medullary cone may be challenging to treat with this approach, probably due to potential peripheral nervous system damage, leaving the open question of whether this large population may benefit from EES. Methods: A T11–T12 SCI patient, with medullary cone involvement, underwent EES implant in a clinical trial (NCT05926843). During three months of testing, we determined optimal stimulation protocols for improving isolated movements and integrated them to reinstate independent walking with a walker. Findings: EES substantially boosted hip flexor, spinal erector, and abdominal muscle contraction, improving the patient's performance in isolated movements. Over three months of combining continuous subthreshold EES with personalized rehabilitation, the patient progressed from being unable to walk to overground ambulation using a two-wheeled walker and bilateral knee and foot orthoses. At the time of hospital discharge, the patient managed to cover 58 m in the 6-min walking test and completed the 10-meter walking test in 40.29 s. Six months after EES implant, the patient was able to walk independently for 1 km with a walker. Conclusions: These results underscore the potential of neurorehabilitation protocols integrating EES also for patients with medullary cone lesions and pave the way for new rehabilitation prospects. Funding: This work was funded by Università Vita-Salute San Raffaele, Boston Scientific Spa, Fondazione Cariplo, Bertarelli Foundation, and the Ministry of University and Research (MUR). © 2025 The Author(s)

关键词： conus medullaris epidural electrical stimulation neuromodulation spinal cord injury spinal cord stimulation Translation to patients trauma

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Tactile sensing with gesture-controlled collaborative robot

Tactile sensing with gesture-controlled collaborative robot

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Workshop on Metrology for Industry 4.0 and IoT

作者： Francesca Sorgini Giuseppe airò Farulla Nikola Lukic Ivan Danilov Loris Roveda Milos Milivojevic Terrin Babu Pulikottil Maria Chiara Carrozza Paolo Prinetto Tullio Tolio Calogero Maria Oddo Petar B. Petrovic Bozica Bojovic Dept. of Excellence in Robotics & A.I. The BioRobotics Institute Scuola Superiore Sant' Anna Pisa Italy Politecnico di Torino Turin Italy Production Engineering Department Faculty of Mechanical Engineering University of Belgrade Belgrade Serbia Istituto Dalle Molle di studi sull'intelligenza artificiale IDSIA-SUPSI Manno Switzerland Faculty of Mechanical Engineering University of Belgrade Belgrade Serbia Ist. di Sistemi e Tecn Industriali per il Manifatturiero Avanzato STIIMA CNR Milano Italy The BioRobotics Institute Scuola Superiore Sant' Anna Pisa Italy Politecnico di Torino CINI AsTech National Lab Torino Italy Politecnico di Milano Milano Italy

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Sensors and human machine interfaces for collaborative robotics will allow smooth interaction in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user's hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

关键词： collaborative robotics telepresence neuromorphic vibrotactile feedback human-robot interaction hand tracking gesture-based teleoperation REMOTE CONTROL Human machine interaction Touch Robot robotic arm Touch Perceptions Haptic Device Component Mechanoreceptors

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Evaluation of Disinfection Methods for Autonomous Mobile Robots Used in Hospital Logistics in Emergency departments

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The Journal of hospital infection 2025年

作者： Przemysław Kardas Filip Bielec Małgorzata Brauncajs Paweł Lewek Dariusz Timler Ewelina Łojewska Marcello Chiurazzi Neri Niccolò Dei Gastone Ciuti Raquel Juliá Ros Víctor Solaz Estevan Alessia Maccaro Leandro Pecchia Beatriz Merino Alejandro Medrano Thomas Penzel Giuseppe Fico Department of Family Medicine Medical University of Lodz Lodz 90419 Poland. Department of Microbiology and Laboratory Medical Immunology Medical University of Lodz Lodz 90419 Poland. Department of Emergency and Disaster Medicine Medical University of Lodz Lodz 90419 Poland. Office for Research Strategies and Development Medical University of Lodz Lodz 90419 Poland. BioRobotics Institute & Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa 56127 Italy. Robotnik Automation S.L. Paterna Valencia 46980 Spain. Department of Social Sciences University of Naples Federico II Applied Biomedical Signal Processing Intelligent eHealth Laboratory School of Engineering University of Warwick Coventry CV4 7AL UK. Applied Biomedical Signal Processing Intelligent eHealth Laboratory School of Engineering University of Warwick Coventry CV4 7AL UK Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome 00128 Italy. Life Supporting Technologies Departamento de Tecnología Fotónica y Bioingeniería Universidad Politècnica de Madrid Madrid Spain. Interdisciplinary Center of Sleep Medicine Charité University Medicine Berlin Berlin Germany.

BACKGROUND:Autonomous Mobile Robots (AMRs) have been increasingly used in hospital logistics, particularly in high-risk areas, such as Emergency departments (EDs), to streamline operations, reduce staff fatigue, and minimize infection risks. However, their effective disinfection remains a critical concern, especially when it comes to solutions aimed at preventing spread of multi-drug-resistant organisms. OBJECTIVE:This study evaluated the microbiological cleanliness and effectiveness of various disinfection methods for AMRs in real-life hospital logistics settings, with a particular focus on their application in EDs. METHODS:The HOSBOT, an AMR designed for hospital logistics, was deployed in a tertiary hospital for two weeks and validated for transport of biological samples. Microbiological contamination was assessed at multiple robot sites before and after disinfection, using two methods, i.e. manual wiping with a standard disinfectant and non-contact fumigation with low temperature vaporized hydrogen peroxide. Contamination levels were evaluated using quantitative and qualitative microbiological techniques, and a threshold of <2.5 colony-forming units/cm for critical environments, recommended by the Centers for Disease Control and Prevention (CDC), was considered as proof of success. RESULTS:Bacterial contamination exceeded thresholds at all sites. Both disinfection methods significantly decreased contamination. Manual wiping reduced bacterial counts below thresholds and eradicated fungal growth, while fumigation was effective for bacterial but not fungal contamination. Fumigation also failed to meet CDC cleanliness standards in hard-to-reach areas. CONCLUSIONS:Both manual wiping and fumigation effectively reduced bacterial contamination, however, wiping showed better results in fungal eradication. Improvements to fumigation methods are necessary, such as application of higher disinfectant concentrations or alternative chemicals. The aforementioned findings not on

关键词： Autonomous Mobile Robots Bacterial contamination Decontamination Disinfection Emergency department Fungal contamination Hospital logistics Intensive care unit Microbiological cleanliness robotics

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Shape Programming: Shape Programming by Modulating Actuation over Hierarchical Length Scales (Adv. Mater. 47/2020)

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Advanced Materials 2020年第47期32卷

作者： Tian Gao Emmanuel Siéfert Antonio DeSimone Benoît Roman PMMH CNRS ESPCI Paris Université PSL Sorbonne Université Université de Paris F‐75005 Paris France MathLab SISSA‐International School for Advanced Studies 34136 Trieste Italy The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna 56127 Pisa Italy

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FV 1 Segregating the prefrontal cortex by means of deep brain stimulation

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Clinical Neurophysiology 2022年 137卷 e1-e2页

作者： B. Hollunder N. Li J.L. Ostrem M. Polosan H. Akram M. Vissani C. Zhang B. Sun C. Finke A.A. Kühn A. Mazzoni L.M. Romito L. Zrinzo E. Joyce S. Chabardes P.A. Starr A. Horn Charité – Universitätsmedizin Berlin Department of Neurology Movement Disorders and Neuromodulation Unit Berlin Germany Charité – Universitätsmedizin Berlin Einstein Center for Neurosciences Berlin Berlin Germany Humboldt-Universität zu Berlin Berlin School of Mind and Brain Berlin Germany University of California San Francisco Department of Neurology Movement Disorders and Neuromodulation Centre San Francisco CA United States Université Grenoble Alpes Grenoble France Inserm U1216 Grenoble Institut des Neurosciences Grenoble France CHU Grenoble Alpes Psychiatry Department Grenoble France UCL Queen Square Institute of Neurology Department of Clinical and Movement Neurosciences London United Kingdom UCL Queen Square Institute of Neurology National Hospital for Neurology and Neurosurgery London United Kingdom Scuola Superiore Sant’Anna The BioRobotics Institute Pisa Italy Scuola Superiore Sant’Anna Department of Excellence in Robotics and AI Pisa Italien Harvard Medical School Massachusetts General Hospital Department of Neurosurgery Boston MA United States Shanghai Jiao Tong University School of Medicine Rujin Hospital Department of Neurosurgery Shanghai People's Republic China Charité – Universitätsmedizin Berlin Department of Neurology Berlin Germany Charité – Universitätsmedizin Berlin NeuroCure Cluster of Excellence Berlin Germany Fondazione IRCCS Istituto Neurologico Carlo Besta Parkinson and Movement Disorders Unit Milan Italien University of California San Francisco Department of Neurological Surgery San Francisco CA United States Brigham & Women’s Hospital Center for Brain Circuit Therapeutics Boston MA United States

Introduction: Neurocircuits are involved in the motor, cognitive, and affective dysfunctions of multiple brain disorders. Functional segregation into these domains has been widely defined on a fronto- cortical basis. However, despite considerable interaction, these functional domains remain partitioned to some extent at (sub)thalamic levels (1). To map the functional organization of the the frontal cortex (FC), we employ optimal connectivity profiles from successful deep brain stimulation (DBS) electrodes to the same target, which receives input from the entire FC, in four different disorders. Specifically, we identify cortical projection sites that, on a group level, associate with maximal improvements following subthalamic nucleus (STN) DBS in dystonia (DYT), Parkinson's disease (PD), Tourette syndrome (TS) or obsessive-compulsive disorder (OCD). Patients and Methods: Using Lead-DBS (2), stimulation volumes were estimated for six patient cohorts from six centers, each implanted with DBS to the STN for treatment of either DYT (N = 62), PD (N = 51), TS (N = 14), or OCD (N = 19). Via DBS Network Mapping (3) we derived disease-specific structural connections between cortex and STN from a normative connectome (4) that relate to optimal clinical outcomes. Results: Functional organization in the STN and FC was reflected along a caudo-rostral gradient (Fig. 1). Beneficial DBS networks for DYT predominantly projected to the primary motor cortex, while in PD, electrode connectivity to pre- and supplementary motor areas, in TS to the dorsomedial preFC, and in OCD to ventromedial, dorsal anterior cingulate and dorsolateral preFC emerged. Conclusion: Our findings demonstrate the usefulness of DBS to investigate the organizational gradient of frontal network (dys)function. As such, they bear both interest for cognitive neurosciences as well as clinical-translational value for circuit-guided DBS. References 1. Haynes WIA, Haber SN (2013): The organization of prefrontal-subthal

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette 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 Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab 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 Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Interactions between marine megafauna and plastic pollution in Southeast Asia

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Science of the Total Environment 2023年 874卷 162502页

作者： Omeyer, Lucy C.M. Duncan, Emily M. Abreo, Neil Angelo S. Acebes, Jo Marie V. AngSinco-Jimenez, Lea A. Anuar, Sabiqah T. Aragones, Lemnuel V. Araujo, Gonzalo Carrasco, Luis R. Chua, Marcus A.H. Cordova, Muhammad R. Dewanti, Lantun P. Espiritu, Emilyn Q. Garay, Jovanie B. Germanov, Elitza S. Getliff, Jade Horcajo-Berna, Eva Ibrahim, Yusof S. Jaafar, Zeehan Janairo, Jose Isagani B. Gyi, Thanda Ko Kreb, Danielle Lim, Cheng Ling Lyons, Youna Mustika, Putu L.K. Neo, Mei Lin Ng, Sirius Z.H. Pasaribu, Buntora Pariatamby, Agamuthu Peter, Cindy Porter, Lindsay Purba, Noir P. Santa Cruz, Ernesto T. Shams, Shahriar Thompson, Kirsten F. Torres, Daniel S. Westerlaken, Rodney Wongtawan, Tuempong Godley, Brendan J. Centre for Ecology and Conservation University of Exeter Penryn Campus Penryn CornwallTR10 9EZ United Kingdom Institute of Marine Sciences-Okeanos University of the Azores Rua Professor Doutor Frederico Machado 4 Horta9901-862 Portugal AI and Robotics Laboratory-Environmental Studies University of the Philippines Mindanao Philippines *** Bohol Jagna Philippines Zoology Division The National Museum of the Philippines Padre Burgos Avenue Manila Philippines Davao Oriental City of Mati Philippines Faculty of Science and Marine Environment Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Marine Mammal Research & Conservation Laboratory Institute of Environmental Science and Meteorology College of Science University of the Philippines Diliman Quezon City Philippines Marine Research and Conservation Foundation Lydeard St Lawrence Somerset United Kingdom Environmental Science Program Department of Biological and Environmental Sciences College of Arts and Sciences Qatar University Doha Qatar Department of Biological Sciences National University of Singapore 14 Science Drive 4 117543 Singapore Lee Kong Chian Natural History Museum National University of Singapore 2 Conservatory Drive Singapore117377 Singapore BRIN Kawasan Jakarta Ancol Jalan Pasir Putih 1 Ancol Timur Jakarta14430 Indonesia Fishery Department Faculty of Fishery and Marine Science Universitas Padjadjaran Bandung40600 Indonesia Department of Environmental Science Ateneo de Manila University Loyola Heights Quezon City1108 Philippines San Isidro Extension Campus Davao Oriental San Isidro Philippines Marine Megafauna Foundation West Palm BeachFL United States Centre for Sustainable Aquatic Ecosystems Harry Butler Institute Murdoch University PerthWA Australia Environmental and Conservation Sciences Murdoch University PerthWA Australia Roctopus ecoTrust Roctopus Dive Sairee Beach Koh Tao 84360 Thailand Juara Turtle Project Kg Juara Pahang Tioman Islan

Southeast (SE) Asia is a highly biodiverse region, yet it is also estimated to cumulatively contribute a third of the total global marine plastic pollution. This threat is known to have adverse impacts on marine megafauna, however, understanding of its impacts has recently been highlighted as a priority for research in the region. To address this knowledge gap, a structured literature review was conducted for species of cartilaginous fishes, marine mammals, marine reptiles, and seabirds present in SE Asia, collating cases on a global scale to allow for comparison, coupled with a regional expert elicitation to gather additional published and grey literature cases which would have been omitted during the structured literature review. Of the 380 marine megafauna species present in SE Asia, but also studied elsewhere, we found that 9.1 % and 4.5 % of all publications documenting plastic entanglement (n = 55) and ingestion (n = 291) were conducted in SE Asian countries. At the species level, published cases of entanglement from SE Asian countries were available for 10 % or less of species within each taxonomic group. Additionally, published ingestion cases were available primarily for marine mammals and were lacking entirely for seabirds in the region. The regional expert elicitation led to entanglement and ingestion cases from SE Asian countries being documented in 10 and 15 additional species respectively, highlighting the utility of a broader approach to data synthesis. While the scale of the plastic pollution in SE Asia is of particular concern for marine ecosystems, knowledge of its interactions and impacts on marine megafauna lags behind other areas of the world, even after the inclusion of a regional expert elicitation. Additional funding to help collate baseline data are critically needed to inform policy and solutions towards limiting the interactions of marine megafauna and plastic pollution in SE Asia. © 2023 The Authors

关键词： Ecosystems

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Common Limitations of Image Processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs 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 Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg 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 Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal 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 Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom 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 Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Author Correction: Rules for the Leg Coordination of Dung Beetle Ball Rolling Behaviour

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Scientific reports 2020年第1期10卷 18182页

作者： Binggwong Leung Nienke Bijma Emily Baird Marie Dacke Stanislav Gorb Poramate Manoonpong Bio-Inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong 21210 Thailand. binggwong.l_s17@vistec.ac.th. Functional Morphology and Biomechanics Zoological Institute Kiel University Kiel 24118 Germany. Division of Functional Morphology Department of Zoology Stockholm University Stockholm SE 10691 Sweden. Department of Biology Lund Vision Group Lund University Sölvegatan 35 223 62 Lund Sweden. Bio-Inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong 21210 Thailand. poramate.m@vistec.ac.th. Embodied AI and Neurorobotics Lab SDU Biorobotics The Mærsk Mc-Kinney Møller Institute The University of Southern Denmark Odense 5230 Denmark. poramate.m@vistec.ac.th.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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