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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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vision System and Depth Processing for DRC-HUBO+

Vision System and Depth Processing for DRC-HUBO+

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IEEE International Conference on robotics and Automation

作者： Inwook Shim Seunghak Shin Yunsu Bok Kyungdon Joo Dong-Geol Choi Joon-Young Lee Jaesik Park Jun-Ho Oh In So Kweon Robotics and Computer Vision Laboratory Dep. of EE KAIST Daejeon 305-701 Korea Adobe Research San Jose CA 95110 United States Intel Labs Santa Clara CA 95054 United States Humanoid Research Center Dep. of ME KAIST Daejeon 305-701 Korea

ISBN: (纸本)9781467380270

This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects and to be robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly handling depth outliers. Our system is suitable for robotic applications in which a robot interacts with the real-world, requiring accurate object detection and pose estimation. We evaluate our depth processing algorithm in comparison with state-of-the-art algorithms on several synthetic and real-world datasets.

关键词： System Depth Processing

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Autonomous underwater panel operation by GIRONA500 UVMS: A practical approach to autonomous underwater manipulation

Autonomous underwater panel operation by GIRONA500 UVMS: A p...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Patryk Cieslak Pere Ridao Mariusz Giergiel Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland Computer Vision and Robotics Research Institute CIRS Building Girona Catalonia Spain

This paper proposes a controller for the GIRONA500 Underwater Vehicle Manipulator System and reports experimental results for an autonomous floating-base valve-turning manipulation application. The selected method is based on kinematic control, avoiding the need for a complex, and difficult to obtain, hydrodynamic model. The method relies on the decoupled control of the vehicle and manipulator velocities using a combination of the task priority redundancy resolution and the task concurrence approaches. The paper discusses the manipulation tasks needed, their hierarchical organisation, and a set of strategies that were needed to complement the `standard' task-priority approach to successfully solve the floating-base manipulation problem in water tank experimental conditions.

关键词： Vehicles Kinematics Manipulators Joints Jacobian matrices Valves

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Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset

Traffic Light Detection: A Learning Algorithm and Evaluation...

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International Conference on Intelligent Transportation

作者： Mark Philip Philipsen Morten Bornø Jensen Andreas Møgelmose Thomas B. Moeslund Mohan M. Trivedi UC San Diego Computer Vision and Robotics Research Laboratory La Jolla CA USA Visual Analysis of People Laboratory Aalborg University Aalborg Denmark

Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to the introduction of learning based detectors using integral channel features. A similar push have not yet been seen for the detection sub-problem of TLR, where detection is dominated by methods based on heuristic models. Evaluation of existing systems is currently limited primarily to small local datasets. In order to provide a common basis for comparing future TLR research an extensive public database is collected based on footage from US roads. The database consists of both test and training data, totaling 46,418 frames and 112,971 annotated traffic lights, captured in continuous sequences under a varying light and weather conditions. The learning based detector achieves an AUC of 0.4 and 0.32 for day sequence 1 and 2, respectively, which is more than an order of magnitude better than the two heuristic model-based detectors.

关键词： Detectors Image color analysis Feature extraction Databases computer vision Intelligent vehicles Roads

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Team-based workshop to engage young students in engineering and science: Building and driving a ROV (R2B2). Two special editions in India

Team-based workshop to engage young students in engineering ...

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OCEANS - Europe

作者： Xavier Cuff Miquel Villanueva Andres El-Fakdi Natalia Hurtós Eduard Muntaner Jordi Freixenet Joan Massich Josep Quintana Narcis Palomeras Anna Bosch Anna Renart Universitat de Girona Girona Catalunya ES Computer Vision and Robotics Group (VICOROB) and UdiGital.Edu Research Group from University of Girona Spain

This work reports the experience gathered along 6 years in developing a team-based project activity to promote engineering programs among secondary school students. The aim of the activity is to increase the interest of students for science and technology in general, but also to promote engineering skills, capabilities and values, leading to attract more secondary school students to enrolment for engineering programs. Simple theoretical concepts related to physics and engineering are illustrated through hands-on experimentation. To achieve this goal, the students build a remotely operated underwater robot in a workshop. The robot is built using low-cost materials and the students customize their own design over the different phases of the workshop. Once the activity is completed, every team understands that with teamwork, effort and a good working strategy, every problem can be overcome. This paper explains in detail the structure and operation of the workshop and introduces a first initiative towards the internationalization of the project, which was carried out in two schools for underprivileged children in South India. Through this initiative in India, the workshop takes a step forward, and in addition to promoting science and technology, it also adds a new dimension of education for global citizenship to the project.

关键词： Conferences Robots Vehicles DC motors Underwater vehicles Buoyancy Buildings

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Day and night-time drive analysis using stereo vision for naturalistic driving studies

Day and night-time drive analysis using stereo vision for na...

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IEEE Symposium on Intelligent Vehicle

作者： Mark P. Philipsen Morten B. Jensen Ravi K. Satzoda Mohan M. Trivedi Andreas Møgelmose Thomas B. Moeslund Computer Vision and Robotics Research Laboratory La Jolla CA USA Visual Analysis of People Laboratory Aalborg University Aal-borg Denmark Aalborg Universitet Aalborg DK

In order to understand dangerous situations in the driving environment, naturalistic driving studies (NDS) are conducted by collecting and analyzing data from sensors looking inside and outside of the car. Manually processing the overwhelming amounts of data that are generated in such studies is very comprehensive. We propose a method for automatic data reduction for NDS based on stereo vision vehicle detection and tracking during day- and nighttime. The developed system can automatically register five NDS events, mainly related to intersections, from an existing NDS dictionary. We propose a new drive event which takes advantage of the extra dimension provided by stereo vision. In total, six drive events are selected on the basis of them being problematic to detect automatically using conventional monocular computer vision approaches. The proposed system is evaluated on day-and nighttime data, resulting in drive analysis report. The proposed system reach an overall precision of 0.78 and an overall recall of 0.72.

关键词： Vehicles Stereo vision Roads Three-dimensional displays Noise Vehicle detection Vehicle crash testing

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Head pose tracking for immersive applications

Head pose tracking for immersive applications

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作者： Henriquez, Pedro Higuera, Oscar Matuszewski, Bogdan J. Robotics and Computer Vision Research Laboratory School of Computing Engineering and Physical Sciences University of Central Lancashire United Kingdom

ISBN: (纸本)9781479957514

The paper describes a 3D head pose tracking system designed for immersive applications. The proposed system is based on a random forest's head detection and pose regression model. The main novel contributions include the application, in immersive technologies, of a head tracker using a range data sensor, the Kalman filter and the random forest, with automatic detection of outliers and handling of the missing data. The envisaged applications of the proposed system include data normalization and data acquisition from remote locations in real-time. The latter is described in detail by integrating 3 degrees of freedom robotic camera head with the proposed head tracker. In this instance the proposed non-contact head tracking system is used to control the robotic camera by replicating measured operator's head motion to improve his/her spatial awareness and sense of immersion, with the captured video shown on a head mounted display. The experimental section includes accuracy and robustness analysis. The system's robustness is examined with respect to the head pose outliers and the missing head detections. The effects of the presence of the head mounted displays on the performance of the system is also assessed. Additionally a brief discussion of operators' psychophysical perception test is also included. © 2014 IEEE.

关键词： Kalman filters

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Co-manipulation with multiple probabilistic virtual guides

Co-manipulation with multiple probabilistic virtual guides

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： G. Raiola X. Lamy F. Stulp FLOWERS Research Team INRIA Bordeaux Sud-Ouest Tal-ence France Robotics and Computer Vision Unite d'Informatique et d'Ingenierie des xENSTA ParisTech Universite Paris-Saclay Palaiseau cedex France CEA-List Gif-sur-Yvette France

In co-manipulation, humans and robots solve manipulation tasks together. Virtual guides are important tools for co-manipulation, as they constrain the movement of the robot to avoid undesirable effects, such as collisions with the environment. Defining virtual guides is often a laborious task requiring expert knowledge. This restricts the usefulness of virtual guides in environments where new tasks may need to be solved, or where multiple tasks need to be solved sequentially, but in an unknown order. To this end, we propose a framework for multiple probabilistic virtual guides, and demonstrate a concrete implementation of such guides using kinesthetic teaching and Gaussian mixture models. Our approach enables non-expert users to design virtual guides through demonstration. Also, they may demonstrate novel guides, even if already known guides are active. Finally, users are able to intuitively select the appropriate guide from a set of guides through physical interaction with the robot. We evaluate our approach in a pick-and-place task, where users are to place objects at one of several positions in a cupboard.

关键词： Probabilistic logic Rails Force Service robots Trajectory Context

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Robotic Software System for the Disaster Circumstances: System of Team KAIST in the DARPA robotics Challenge Finals

Robotic Software System for the Disaster Circumstances: Syst...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jeongsoo Lim Inwook Shim Okkee Sim Hyunmin Joe Inhyeok Kim Jungho Lee Jun-Ho Oh Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Robotics and Computer Vision Laboratory Department of Electrical Engineering College of Information Science and Technology Korea Advanced Institute of Science and Technology Rainbow Co.

ISBN: (纸本)9781479968862

We developed a software system for operating the humanoid robot DRC-HUBO+ in disaster circumstances that the US Defense Advanced research Projects Agency suggested. This system was developed under the consideration of the stability of whole system, the systemic software environment for multiple developers, and the recovery routine when the system encounters a seriously abnormal situation. With these design goals, we devised our strategy for three domains: system, vision, and communication. Following the strategy we made two software frameworks (PODO and VPC) and one logical flow of data for remote process management. With this software system, we conducted all Tasks in the DARPA robotics Challenge Finals, and we won the competition.

关键词： Software systems DARPA design target robotics Disasters Religious Missions flow datas

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Autonomous I-AUV docking for fixed-base manipulation 19

Autonomous I-AUV docking for fixed-base manipulation

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Palomeras, Narcís Ridao, Pere Ribas, David Vallicrosa, Guillem Computer Vision and Robotics Research Institute Scientific and Technological Park of the University of Girona CIRS Building C/Pic de Peguera Girona CataloniaCO 17071 Spain

ISBN: (纸本)9783902823625

While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist demanding intervention capabilities. This is the case, for instance, of the maintenance of permanent underwater observatories or submerged oil wells. These tasks, currently undertaken by remotely operated vehicles (ROVs), can be automated using intervention AUVs (I-AUVs) reducing their complexity and costs. The TRITON spanish funded project proposes the use of light I-AUV for autonomous intervention tasks, such as valve turning or connector pluging/unpluging, in adapted sub-sea infrastructures. To this aim, this paper presents the design and implementation of an I-AUV-friendly sub-sea docking panel, as well as the vision-based autonomous docking procedure for the Girona 500 lightweight I-AUV. The panel implements a funnel-based docking method for passive accommodation. It also includes a T valve and a custom designed hot stab connector. Once docked, the I-AUV and the panel become rigid and basic fixed-base manipulation strategies can be used for manipulation. © IFAC.

关键词： Autonomous underwater vehicles

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