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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

Magnetic-visual sensor fusion-based dense 3D reconstruction and localization for endoscopic capsule robots

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arXiv 2018年

作者： Turan, Mehmet Almalioglu, Yasin Ornek, Evin Pinar Araujo, Helder Yanik, Mehmet Fatih Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Science Department University of Oxford Oxford United Kingdom Informatics Department Technical University of Muenich Germany Institute for Systems and Robotics University of Coimbra Portugal Department of Information Technology and Electrical Engineering Zurich Switzerland

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly realtime, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot applications

A multi-class ramp metering and routing control scheme to reduce congestion and traffic emissions in freeway networks

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IFAC-PapersOnLine 2016年第3期49卷 329-334页

作者： Pasquale, C. Sacone, S. Siri, S. De Schutter, B. Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Delft Center for Systems and Control Delft University of Technology Netherlands

In this paper, a multi-class and multi-objective combined ramp metering and routing control strategy is proposed to improve performance in freeway traffic networks. The control strategy is of the multi-class type, i.e. different classes of vehicles are taken into account, so that specific control policies can be devised for each class. Moreover, ramp metering and route guidance are properly coordinated in order to reduce the total travel time and the total emissions in the freeway system in a balanced way. The controller proposed in the paper is of the predictive feedback type, i.e. the control computed at each time step depends on the measured system state and on the prediction of the system evolution, considering both a traffic and an emission model. Some simulation results are presented showing the effectiveness of the proposed control scheme. © 2016

关键词： Traffic congestion Highway traffic control Network routing Travel time Control strategies Different class Freeway networks Improve performance Measured system System evolution Total emissions Traffic emissions

A planning approach for sizing the capacity of a port rail system: scenario analysis applied to La Spezia port network

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IFAC-PapersOnLine 2016年第3期49卷 371-376页

作者： Fioribello, S. Caballini, C. Sacone, S. Siri, S. Sogemar Contship Italia Group Melzo Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy

This paper presents an optimization approach for sizing the capacity of port rail networks, in terms of maximum number of trains that can be managed over a certain time horizon. The proposed optimization method is based on a discrete-time model of the overall system in order to represent the shunting operations in the port rail network. The resulting MILP optimization problem has been applied to a real case study referred to the port rail network of La Spezia Container Terminal, in Northern Italy. What-if analyses have been carried out to test the system potentiality by varying some parameters, i.e. the terminal equipment productivity, the number of locomotives and the time to perform some technical operations. © 2016

关键词： Railroad transportation

Scene learning, recognition and similarity detection in a fuzzy ontology via human examples

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arXiv 2017年

作者： Buoncompagni, Luca Mastrogiovanni, Fulvio Saffiotti, Alessandro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy Centre for Applied Autonomous Sensor Systems Örebro University Sweden

This paper introduces a Fuzzy Logic framework for scene learning, recognition and similarity detection, where scenes are taught via human examples. The framework allows a robot to: (i) deal with the intrinsic vagueness associated with determining spatial relations among objects;(ii) infer similarities and dissimilarities in a set of scenes, and represent them in a hierarchical structure represented in a Fuzzy ontology. In this paper, we briefly formalize our approach and we provide a few use cases by way of illustration. Nevertheless, we discuss how the framework can be used in real-world scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Fuzzy logic

WhoLoDancE: Whole-body interaction learning for dance education

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WhoLoDancE: Whole-body interaction learning for dance educat...

2018 Workshop on Cultural informatics, CI 2018

作者： Rizzo, Anna El Raheb, Katerina Whatley, Sarah Cisneros, Rosa Maria Zanoni, Massimiliano Camurri, Antonio Viro, Vladimir Matos, Jean-Marc Piana, Stefano Buccoli, Michele Markatzi, Amalia Palacio, Pablo Even Zohar, Oshri Sarti, Augusto Ioannidis, Yannis Fletcher, Edwin-Morley Lynkeus Rome Italy Athena Research Centre Athens Greece Centre for Dance Research Coventry University United Kingdom Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy Department of Informatics Bioengineering Robotics and System Engineering University of Genova Genova Italy Peachnote GmbH Munich Germany Lykeion Ton Hellenidon Athens Greece K. Danse Toulouse France Instituto Stocos Madrid Spain Motek Entertainment Amsterdam Netherlands

Dance resides among the most ancestral forms of art, representing a major asset of the human intangible cultural heritage playing, at the same time, a primary role in contemporary artistic creation. WhoLoDancE, a Research and Innovation Action funded under the European Union’s Horizon 2020 research and innovation programme, aimed at the double goal of preserving its inheritance and integrating digital technologies into contemporary dance learning, teaching and choreography through the digitalisation of dance movements with motion capture techniques, the creation of a large motion repository - including movements from ballet, contemporary, flamenco and traditional Greek folk dances - and the implementation of breakthrough applications ranging from movement quality annotation and segmentation, similarity search, movement blending, multimodal and virtual reality-based experiences for self-reflection and experimentation. In this paper, we present the prototype tools and state-of-the-art results of the project development in its conclusive phase, highlighting the added value this interdisciplinary approach could possibly bring to dance learning and practice, the main technical, practical and cultural challenges encountered in this path and open issues to be addressed in the months to come, providing hints on future research directions. Copyright held by the author(s).

关键词： Virtual reality

A simulated annealing approach for the capacitated dynamic lot sizing problem in a closed remanufacturing system

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IFAC-PapersOnLine 2016年第12期49卷 1496-1501页

作者： Roshani, Abdolreza Giglio, Davide Paolucci, Massimo Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via Opera Pia 13 Genova16145 Italy

The capacitated dynamic lot sizing problem with product returns in a closed remanufacturing system is addressed in this paper. The system is designed to satisfy the demands of different classes of single level products by remanufacturing end-life returned products. A single machine with a limited capacity in each time period is used to perform the remanufacturing operations. A mathematical programming formulation is proposed for the considered problem: the proposed model minimizes the sum of remanufacturing costs over a finite planning horizon. The problem is a generalized version of the classical capacitated dynamic lot sizing problem, and thus it is NP-hard itself. Therefore, a simulated annealing algorithm, with an efficient neighborhood generation which takes into account the constraints of the problem, is proposed as a solution approach. To evaluate the efficiency of the proposed algorithm, a set of experimental instances are generated and solved. The comparison between the results obtained with the proposed simulated annealing approach and the ones generated by the CPLEX solver shows the effectiveness of the proposed algorithm. © 2016

关键词： Simulated annealing Mathematical programming Optimization Production control Scheduling algorithms Capacitated lot sizing Dynamic lot sizing Finite planning horizon Meta heuristics Production Planning Remanufacturing Remanufacturing system Simulated annealing algorithms

A comparison of nonsmooth, nonconvex, constrained optimization solvers for the design of time-delay compensators

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arXiv 2018年

作者： Kungurtsev, Vyacheslav Mitchell, Tim Vyhlídal, Tomáš Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

We present a detailed set of performance comparisons of two state-of-the-art solvers for the application of designing time-delay compensators, an important problem in the field of robust control. Formulating such robust control mechanics as constrained optimization problems often involves objective and constraint functions that are both nonconvex and nonsmooth, both of which present significant challenges to many solvers and their end-users hoping to obtain good solutions to these problems. In our particular engineering task, the main difficulty in the optimization arises in a nonsmooth and nonconvex stability constraint, which states that the infinite spectrum of zeros of the so-called shaper should remain in the open left half-plane. To perform our evaluation, we make use β-relative minimization profiles, recently introduced visualization tools that are particularly suited for benchmarking solvers on nonsmooth, nonconvex, constrained optimization problems. Furthermore, we also introduce new visualization tools, called Global-Local Profiles, which for a given problem and a fixed computational budget, assess the tradeoffs of distributing the budget over few or many starting points, with the former getting more budget per point and latter less. Copyright © 2018, The Authors. All rights reserved.

关键词： Robust control