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3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002

作者： Kim, Myoung-Hee Choi, Soo-Mi Rhee, Seon-Min Kwon, Doo-Young Kim, Hyo-Sun Ewha Womans Univ Dept. of Computer Science and Engineering 11-1 Daehyun-dong Seodaemun-gu Seoul Korea Republic of Ewha Womans Univ Center for Computer Graphics and Virtual Reality 11-1 Daehyundong Seodaemun-gu Seoul Korea Republic of Sejong Univ Dept. of Software Engineering 98 Gunja-dong Gwangjin-gu Seoul Korea Republic of

ISBN: (纸本)3540002626

In this paper, we present a guided interaction approach for architectural design in a table-type VR environment (virtual table). By making it easier to communicate between two or more people in a virtual space, the virtual table is effectively used in architectural visualization, medical visualization, and scientific visualization. However, most applications provide simple functions with easy handling or focus on visualization because accurate interaction on the virtual table is not easy. We developed a table-type VR tool for architectural design using guided 3-D interaction techniques. It consists of the interactive VR modeler, the architectural interpreter and the hybrid tracker. We also introduce architectural design process from the very initial stage to the final one by using our tool. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Architectural design

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Advanced illumination techniques for GPU-based volume raycasting 09

Advanced illumination techniques for GPU-based volume raycas...

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ACM SIGGRAPH 2009 Courses, SIGGRAPH '09

作者： Hadwiger, Markus Ljung, Patric Salama, Christof Rezk Ropinski, Timo VRVis Research Center for Virtual Reality and Visualization Donau-City-Straße 1 A-1220 Vienna Austria Siemens Corporate Research Imaging and Visualization Department 755 College Road East Princeton NJ 08540 United States Computer Graphics Group University of Siegen Hölderlinstr. 3 57068 Siegen Germany Visualization and Computer Graphics Research Group University of Münster Einsteinstr. 62 48149 Münster Germany

ISBN: (纸本)9781450379380

Volume raycasting techniques are important for both visual arts and visualization. They allow efficient generation of visual effects and visualization of scientific data obtained by tomography or numerical simulation. Volume-rendering techniques are also effective for direct rendering of implicit surfaces used for soft-body animation and constructive solid geometry. The focus of this course is on volumetric illumination techniques that approximate physically based light transport in participating media. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion, and simple Monte Carlo-based approaches to global illumination, including translucency and scattering.

关键词： Visualization

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Intuitive virtual grasping for non haptic environments

Intuitive virtual grasping for non haptic environments

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Pacific Conference on computer graphics and Applications

作者： T. Ullmann J. Sauer Department of Computer Science and Automation Computer Graphics Program Technical University Ilmenau Ilmenau Germany Commercial Vehicle Development DaimlerChrysler Aerospace Sindelfingen Germany Virtual Reality Competence Center Daimler Chrysler Research and Technology Center Ulm Germany

This paper presents an approach for grasping virtual objects in virtual environments with a data glove. Our focus lies on developing new methods that allow sensible realistic grasping gestures which correspond to human grasping behavior. To meet the demands of industrial applications, users must be able to grasp an object in an intuitive way with one or two hands and, beyond that, manipulate grasped objects between their fingers. To avoid the use of expensive force or touch feedback hardware, but also to ensure a safe grip on virtual objects, several forms of visual feedback are provided. First, we describe the basic algorithm for grasping with one or two hands and finally, we present a method to precisely manipulate the grasped objects between the virtual fingers.

关键词： Grasping Haptic interfaces Data gloves virtual reality virtual environment Fingers Force feedback Hardware Application software Engines

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Metabolic visualization and intelligent shape analysis of the hippocampus

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International Conference on Computational Intelligence and Security, CIS 2005

作者： Choi, Yoo-Joo Kim, Jeong-Sik Kim, Min-Jeong Choi, Soo-Mi Kim, Myoung-Hee Institute for Graphic Interfaces Ewha-SK Telecom Building 11-1 Daehyun-Dong Seodaemun-Ku Seoul Korea Republic of Department of Computer Science and Engineering Ewha Womans University Daehyun-Dong Seodaemun-Ku Seoul Korea Republic of Center for Computer Graphics and Virtual Reality Ewha Womans University Daehyun-Dong Seodaemun-Ku Seoul Korea Republic of School of Computer Engineering Sejong University Seoul Korea Republic of Department of Computer Application Technology Seoul University of Venture and Information Seoul Korea Republic of

ISBN: (纸本)3540308199

This paper suggests a prototype system for visualization and analysis of anatomic shape and functional features of the hippocampus. Based on the result of MR-SPECT multi-modality image registration, anatomical and functional features of hippocampus are extracted from MR and registered SPECT images, respectively. The hippocampus is visualized in 3D by applying volume rendering to hippocampus volume data extracted from the MR image with color coded by registered SPECT image. In order to offer the objective and quantitative data concerning to the anatomic shape and functional features of the hippocampus, the geometric volume and the SPECT intensity histogram of hippocampus regions are automatically measured based on the MR and the registered SPECT image, respectively. We also propose a new method for the analysis of hippocampal shape using an integrated Octree-based representation, consisting of meshes, voxels, and skeletons. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image analysis

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MultiAR: A Multi-User Augmented reality Platform for Biomedical Education

MultiAR: A Multi-User Augmented Reality Platform for Biomedi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Markus Perz Gijs Luijten Jens Kleesiek Dieter Schmalstieg Jan Egger Christina Gsaxner Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine University Medicine Essen Essen Germany Cancer Research Center Cologne Essen West German Cancer Center Essen Germany Institute for Visualization and Interactive Systems University of Stuttgart Stuttgart Germany Virtual and Extended Reality in Medicine (ZvRM) University Hospital Essen Essen Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper addresses the growing integration of Augmented reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile, domain-specific platform enabling multi-user interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including handheld and head-mounted options. The framework extends across domains, augmenting biomedical education applications with collaborative capabilities. We define essential requirements for a multi-user AR framework in education, detail MultiAR’s design and implementation, and comprehensively evaluate it using anatomy education examples. Quantitative and qualitative analyses, covering system performance, accuracy metrics, and a user study with 20 participants, highlight the urgent need for a tailored collaborative AR platform in biomedical education. Results underscore enthusiasm for collaborative AR technology, endorsing MultiAR as an accessible, versatile solution for developers and end-users in biomedical education.

关键词： Measurement Accuracy Federated learning System performance Education Collaboration Anatomy Engineering in medicine and biology Augmented reality

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Interaction-enhanced Simulation Workspace for Immersive Collaboration

Interaction-enhanced Simulation Workspace for Immersive Coll...

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Asian Simulation Conference/The 6th International conference on system Simulation and Scientific Computing

作者： Myoung-Hee Kim Seon-Min Rhee Jiyoung Park Ju-Haye Kim Eun Cho Hyo-Sun You Dept. of Computer Science and Engineering Ewha Womans University Seoul Kerea Center for Computer Graphics and Virtual Reality Ewha Womans University Seoul Kerea Dept. of Computer Science and Engineering Ewha Womans University Seoul Kerea Dept. of Computer Science and Engineering Ewha Womans University Seoul Kerea Dept. of Computer Science and Engineering Ewha Womans University Seoul Kerea Dept. of Computer Science and Engineering Ewha Womans University Seoul Kerea Dept. of Computer Science and Engineering Ewha Womans University Seoul Kerea

A large surround display such as a CAVETM-like system can be used as an immersive and collaborative simulation workspace, since it maximizes visual realism by a stereo effect. However, methods for working interactively in a 3D workspace rather than on a 2D display are not yet sufficiently developed. We suggest three interaction techniques to enhance a simulation workspace for immersive collaboration. The transparent panel interface is an intermediate medium which helps direct interaction such as object selection, manipulation and navigation. A miniature of the virtual world is shown through the panel, and the user interacts with that instead of with virtual objects which may be located far from them. The visual guider provides depth information which allows a user to know the distance from a virtual object and where they are in a large virtual environment. Vision-based tracking using IR makes it possible to interact naturally and intuitively with objects.

关键词： virtual reality, human computer interaction, immersive simulation environment, interactive simulation workspace.

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Generalisation of Segmentation Using Generative Adversarial Networks

Generalisation of Segmentation Using Generative Adversarial ...

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IEEE International Symposium on Biomedical Imaging

作者： André Ferreira Gijs Luijten Behrus Puladi Jens Kleesiek Victor Alves Jan Egger Institute for AI in Medicine (IKIM) University Hospital Essen (AöR) Essen Germany Center Algoritmi / LASI University of Minho Braga Portugal Computer Algorithms for Medicine Laboratory Graz Austria Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Institute of Computer Graphics and Vision (ICG) Graz University of Technology Graz Austria Cancer Research Center Cologne Essen (CCCE) West German Cancer Center Essen (WTZ) University Hospital Essen (AöR) Essen Germany Partner Site Essen German Cancer Consortium (DKTK) Essen Germany Department of Physics TU Dortmund University Dortmund Germany Center for Virtual and Extended Reality in Medicine (ZvRM) University Hospital Essen Essen Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

State-of-the-art deep learning algorithms are easily biased and evaluated in misleading scenarios, especially in the medical context, where scenarios change rapidly and diseases develop quickly. The BraTS 2024 GoAT challenge aims to evaluate how brain tumour segmentation algorithms can adapt to different circumstances when these are not available for training. Our solution utilises state-of-the-art conditional generative adversarial networks to generate realistic new cases and train a segmentation algorithm that takes advantage of the convolutions and attention mechanisms. Our solution achieved a DSC value of 0.855, 0.863, 0.883 and an HD95 value of 24.83, 24.10 and 21.72 for the enhancing tumour, the tumour core and the whole tumour in the validation set, respectively.

关键词： Training Knowledge engineering Deep learning Generative adversarial networks Robustness Inference algorithms Tumors

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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