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

arXiv

引用

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

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Stereo matching with color and monochrome cameras in low-light conditions

Stereo matching with color and monochrome cameras in low-lig...

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2016 IEEE Conference on computer vision and Pattern Recognition, CVPR 2016

作者： Jeon, Hae-Gon Lee, Joon-Young Im, Sunghoon Ha, Hyowon Kweon, In So Robotics and Computer Vision Lab. KAIST Korea Republic of Adobe Research United States

ISBN: (纸本)9781467388511

Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two crossspectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

关键词： Stereo image processing

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Magnetic-visual sensor fusion based medical SLAM for endoscopic capsule robot

arXiv

引用

arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Gilbert, Hunter Araujo, Helder Konukoglu, Ender Sitti, Metin Max Planck Institute for Intelligent Systems Stuttgart Germany Computer Vision Laboratory Eth Zurich Computer Engineering Department Bogazici University Louisiana State University Baton RougeLA United States Robotics Laboratory University of Coimbra

A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots. These robots are an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a dense, non-rigidly deformable, and real-time map fusion approach for actively controlled endoscopic capsule robot applications. The method combines magnetic and vision based localization, and makes use of frame-to-model fusion and model-to-model loop closure. The performance of the method is demonstrated using an ex-vivo porcine stomach model. Across four trajectories of varying speed and complexity, and across three cameras, the root mean square localization errors range from 0.42 to 1.92 cm, and the root mean square surface reconstruction errors range from 1.23 to 2.39 cm. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetism

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Evaluation of dense 3D reconstruction from 2D face images in the wild

arXiv

引用

arXiv 2018年

作者： Feng, Zhen-Hua Huber, Patrik Kittler, Josef Hancock, Peter J.B. Wu, Xiao-Jun Zhao, Qijun Koppen, Paul Rätsch, Matthias Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom Faculty of Natural Sciences University of Stirling StirlingFK9 4LA United Kingdom School of Internet of Things Engineering Jiangnan University Wuxi214122 China Biometrics Research Lab College of Computer Science Sichuan University Chengdu610065 China Image Understanding and Interactive Robotics Reutlingen University Reutlingen72762 Germany

This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by five state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition. This is an extended version of the original conference submission with two additional 3D face reconstruction systems evaluated on the benchmark. Copyright © 2018, The Authors. All rights reserved.

关键词： Petroleum reservoir evaluation

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Augmented Cognition via Brainwave Entrainment in Virtual Reality: An Open, Integrated Brain Augmentation in a Neuroscience System Approach

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Augmented Human Research 2017年第1期2卷 1-14页

作者： Emanuele Argento George Papagiannakis Eva Baka Michail Maniadakis Panos Trahanias Michael Sfakianakis Ioannis Nestoros Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology Heraklion Greece Department of Computer Science University of Crete Heraklion Greece Department of Medicine University of Greece Heraklion Greece Department of Business Administration University of Piraeus Piraeus Greece Synchronal Amphiaraia University of Crete Snip-off Company Athens Greece

Building on augmented cognition theory and technology, our novel contribution in this work enables accelerated, certain brain functions related to task performance as well as their enhancement. We integrated in an open-source framework, latest immersive virtual reality (VR) head-mounted displays, with the Emotiv EPOC EEG headset in an open neuro- and biofeedback system for cognitive state detection and augmentation. Our novel methodology allows to significantly accelerate content presentation in immersive VR, while lowering brain frequency at alpha level—without losing content retention by the user. In our pilot experiments, we tested our innovative VR platform by presenting to N = 25 subjects a complex 3D maze test and different learning procedures for them on how to exit it. The subjects exposed to our VR-induced entrainment learning technology performed significantly better than those exposed to other “classical” learning procedures. In particular, cognitive task performance augmentation was measured for: learning time, complex navigational skills and decision-making abilities, orientation ability.

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iCLAP: Shape recognition by combining proprioception and touch sensing

arXiv

引用

arXiv 2018年

作者： Luo, Shan Mou, Wenxuan Althoefer, Kaspar Liu, Hongbin Center for Robotics Research Department of Informatics King's College London LondonWC2R 2LS United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Multimedia and Vision Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom

For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions;hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability. Copyright © 2018, The Authors. All rights reserved.

关键词： Object recognition

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AEROSTACK: An architecture and open-source software framework for aerial robotics

AEROSTACK: An architecture and open-source software framewor...

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2016 International Conference on Unmanned Aircraft Systems, ICUAS 2016

作者： Sanchez-Lopez, Jose Luis Fernandez, Ramon A. Suarez Bavle, Hriday Sampedro, Carlos Molina, Martin Pestana, Jesus Campoy, Pascual Computer Vision Group Centre for Automation and Robotics CSIC UPM Spain Department of Artificial Intelligence Technical University of Madrid UPM Spain

ISBN: (纸本)9781467393331

To simplify the usage of the Unmanned Aerial Systems (UAS), extending their use to a great number of applications, fully autonomous operation is needed. There are many open-source architecture frameworks for UAS that claim the autonomous operation of UAS, but they still have two main open issues: (1) level of autonomy, being in most of the cases limited and (2) versatility, being most of them designed specifically for some applications or aerial platforms. As a response to these needs and issues, this paper presents Aerostack, a system architecture and open-source multi-purpose software framework for autonomous multi-UAS operation. To provide higher degrees of autonomy, Aerostack's system architecture integrates state of the art concepts of intelligent, cognitive and social robotics, based on five layers: reactive, executive, deliberative, reflective, and social. To be a highly versatile practical solution, Aerostack's open-source software framework includes the main components to execute the architecture for fully autonomous missions of swarms of UAS;a collection of ready-to-use and flight proven modular components that can be reused by the users and developers;and compatibility with five well known aerial platforms, as well as a high number of sensors. Aerostack has been validated during three years by its successful use on many research projects, international competitions and exhibitions. To corroborate this fact, this paper also presents Aerostack carrying out a fictional fully autonomous indoors search and rescue mission. © 2016 IEEE.

关键词： Open source software

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Do Sensory Preferences of Children with Autism Impact an lmitation Task with a Robot?

Do Sensory Preferences of Children with Autism Impact an lmi...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Pauline Chevalier Gennaro Raiola Jean-Claude Martin Brice Isableu Christophe Bazile Adriana Tapus U2IS Robotics & Computer Vision Lab Palaiseau France LIMSI Université Paris-Saclay Orsay France PSYCLE Aix-Marseille Univ Aix-en France Groupement des Associations Sociale France

ISBN: (纸本)9781450343367

Imitation is of major importance during social interactions, would it be between humans or between a human and a robot. This is even more true when considering users with special needs. In this paper, we describe an experimental imitation task protocol using a robot Nao that we designed to assess whether sensory profiles of children with Autistic Spectrum Disorder (ASD) influence their capabilities to imitate or to initiate gestures that are going to be imitated. We based our work on the hypothesis that children with an overreliance on proprioceptive cues and hyporeactivity to visual cues have a greater difficulty imitating and improve their skills more slowly than children with an overreliance on visual cues and hyporeactivity to proprioceptive cues. Twelve children and teenagers with ASD participated in seven imitation sessions over eight weeks. As expected, we observed that children with an overreliance on proprioceptive cues and hyporeactivity to visual cues had more difficulties imitating the robot than the other children. Moreover, the repeated sessions revealed to have positive effects on social behaviors displayed by all children (gaze to the partner, imitations) toward a human partner after the sessions with the robot. We conclude on the possible impacts of such results on the design of social human-robot interactions for users with ASD.

关键词： Visualization Variable speed drives Task analysis Robot sensing systems Autism Medical treatment

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Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

arXiv

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： vision

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Effectiveness of socially assistive robotics during cognitive stimulation interventions: Impact on caregivers

Effectiveness of socially assistive robotics during cognitiv...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jainendra Shukla Miguel Barreda-Ángeles Joan Oliver Domènec Puig Intelligent Robotics and Computer Vision Group (IRCV) Rovira i Virgili University Tarragona Spain Instituto de Robótica para la Dependencia Sitges Barcelona Spain Eurecat Technology Centre of Catalonia Barcelona Spain

Execution of cognitive stimulation interventions for cognitive training of individuals in need represents significant burden on caregivers in time and labor costs. Recent advancements in Socially Assistive robotics (SAR) research can be exploited to reduce caregivers burden by work sharing with robots and supplementing/complementing human resources in execution of interventions. Current research evaluates the effectiveness of the SAR empowered cognitive training activity of Bingo Musical among thirty individuals with ID in multi-center trials. A multidimensional evaluation of caregivers workload was conducted; including subjective workload, time spent on users personalized interventions, and qualitative interviews with caregivers. The results of the research confirm a significant reduction in caregivers burden and raise a concern about the need of a specific training of the caregivers to take maximum advantage of SAR in health care.

关键词： Robot kinematics Radiofrequency identification Training Monitoring Rehabilitation robotics

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