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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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A virtual reality platform for dynamic human-scene interaction 16

A virtual reality platform for dynamic human-scene interacti...

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2016 SIGGRAPH ASIA Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, SA 2016

作者： Lin, Jenny Guo, Xingwen Shao, Jingyu Jiang, Chenfanfu Zhu, Yixin Zhu, Song-Chun UCLA Center for Vision Cognition Learning and Autonomy United States University of Hong Kong Electrical and Electronic Engineering Department Hong Kong UCLA Computer Graphics and Vision Laboratory United States

ISBN: (纸本)9781450345484

Both synthetic static and simulated dynamic 3D scene data is highly useful in the fields of computer vision and robot task planning. Yet their virtual nature makes it difficult for real agents to interact with such data in an intuitive way. Thus currently available datasets are either static or greatly simplified in terms of interactions and dynamics. In this paper, we propose a system in which Virtual Reality and human/finger pose tracking is integrated to allow agents to interact with virtual environments in real time. Segmented object and scene data is used to construct a scene within Unreal Engine 4, a physics-based game engine. We then use the Oculus Rift headset with a Kinect sensor, Leap Motion controller and a dance pad to navigate and manipulate objects inside synthetic scenes in real time. We demonstrate how our system can be used to construct a multi-jointed agent representation as well as fine-grained finger pose. In the end, we propose how our system can be used for robot task planning and image semantic segmentation. © 2016 Copyright held by the owner/author(s). SA '16, December 05-08 2016.

关键词： Virtual reality

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Predicting when saliency maps are accurate and eye fixations consistent

Predicting when saliency maps are accurate and eye fixations...

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

作者： Volokitin, Anna Gygli, Michael Boix, Xavier Computer Vision Laboratory ETH Zurich Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore CBMM Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781467388511

Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps. Recently, the success of Deep Convolutional Neural Networks (DCNNs) for object recognition has opened a new avenue for computational models of visual attention due to the tight link between visual attention and object recognition. In this paper, we show that using features from DCNNs for object recognition we can make predictions that enrich the information provided by saliency models. Namely, we can estimate the reliability of a saliency model from the raw image, which serves as a meta-saliency measure that may be used to select the best saliency algorithm for an image. Analogously, the consistency of the eye fixations among subjects, i.e. the agreement between the eye fixation locations of different subjects, can also be predicted and used by a designer to assess whether subjects reach a consensus about salient image locations.

关键词： Behavioral research

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Predicting When Saliency Maps are Accurate and Eye Fixations Consistent

Predicting When Saliency Maps are Accurate and Eye Fixations...

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IEEE Conference on computer vision and Pattern Recognition

作者： Anna Volokitin Michael Gygli Xavier Boix Computer Vision Laboratory ETH Zurich Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781467388528

关键词： Ocular Fixation object recognition Visual Attention Computational modeling maps Designers

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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

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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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Erratum: Ultrasound-driven 4D MRI (2018 Phys. Med. Biol. 63 145015)

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Physics in Medicine & Biology 2018年第17期63卷

作者： Alina Giger Marc Stadelmann Frank Preiswerk Christoph Jud Valeria De Luca Zarko Celicanin Oliver Bieri Rares Salomir Philippe C Cattin Department of Biomedical Engineering University of Basel Allschwil Switzerland Center for Medical Image Analysis & Navigation University of Basel Allschwil Switzerland Institute for Surgical Technology & Biomechanics University of Bern Bern Switzerland Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA United States of America Computer Vision Laboratory ETH Zurich Zurich Switzerland Now at the Novartis Institute for BioMedical Research Basel Switzerland Division of Radiological Physics Department of Radiology University of Basel Hospital Basel Switzerland Image Guided Interventions Laboratory Faculty of Medicine Radiology University of Geneva Geneva Switzerland

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Optimization Techniques in computer vision 1

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丛书名： Advances in computer vision and Pattern Recognition

2016年

作者： Mongi A. Abidi Andrei V. Gribok Joonki Paik

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Convex cone volume analysis for finding endmembers in hyperspectral imagery

Convex cone volume analysis for finding endmembers in hypers...

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作者： Chang, Chein-I Xiong, Wei Chen, Shih-Yu Information and Technology College Dalian Maritime University Dalian China School of Physics and Optoelectronic Engineering Xidian University Xian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

This paper presents a new approach, called convex cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be shown that finding the maximal volume of a convex cone in the original data space is equivalent to finding the maximal volume of a simplex in a hyperplane. As a result, the CCVA can take advantage of many recently developed fast computational algorithms developed for N-FINDR to derive their counterparts for CCVA. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Spectroscopy

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Multi-modal multi-scale deep learning for large-scale image annotation

arXiv

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arXiv 2017年

作者： Niu, Yulei Lu, Zhiwu Wen, Ji-Rong Xiang, Tao Chang, Shih-Fu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China School of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Department of Electrical Engineering Columbia University New YorkNY10027 United States

image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept;2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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PGP2X: Principal geometric primitives parameters extraction 10

PGP2X: Principal geometric primitives parameters extraction

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10th International Conference on computer Graphics Theory and Applications, GRAPP 2015

作者： Toony, Zahra Laurendeau, Denis Gagné, Christian Computer Vision and System Laboratory Department of Electrical and Computer Engineering Université Laval QuébecQC Canada

ISBN: (纸本)9789897580871

In reverse engineering, it is important to extract the 3D geometric primitives that compose an object. It is also important to find the values of the parameters describing each primitive. This paper presents an approach for the estimation of the parameters of geometric primitives once their type is known using 3D information. The primitives of interest are planes, spheres, cylinders, cones, tori and partial instances of the latter four types. The proposed approach extends methods found in the literature for planes, spheres, cylinders and cones and proposes a new method for dealing with tori. The results of the proposed method are compared to approaches found in the literature as well as with ground truth values. The proposed method can be applied to the estimation of parameters of geometric primitives of synthetic CAD models as well as for models of real objects acquired with 3D scanners. Copyright © 2015 SCITEPRESS - Science and Technology Publications. All rights reserved.

关键词： Principal component analysis

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