Eurographics Workshop on Visual Computing for Biomedicine最新文献_第5页

Feature Exploration using Local Frequency Distributions in Computed Tomography Data 利用计算机断层扫描数据的局部频率分布进行特征探索

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2020-01-01 DOI: 10.2312/vcbm.20201166

M. Falk, P. Ljung, C. Lundström, A. Ynnerman, I. Hotz

{"title":"Feature Exploration using Local Frequency Distributions in Computed Tomography Data","authors":"M. Falk, P. Ljung, C. Lundström, A. Ynnerman, I. Hotz","doi":"10.2312/vcbm.20201166","DOIUrl":"https://doi.org/10.2312/vcbm.20201166","url":null,"abstract":"Frequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transfer function (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly in low-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to the area of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of local frequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allows us to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplify the exploration step. We propose three approaches for data exploration which we illustrate with several visualization cases highlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstrate the power of the method on selected datasets. CCS Concepts • Human-centered computing → Scientific visualization; Visualization techniques; • Applied computing → Life and medical sciences;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"221 1","pages":"13-24"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76972261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing Protein Similarity by Clustering Molecular Surface Maps 聚类分子表面图分析蛋白质相似性

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2020-01-01 DOI: 10.2312/vcbm.20201177

Karsten Schatz, Florian Friess, M. Schäfer, T. Ertl, M. Krone

{"title":"Analyzing Protein Similarity by Clustering Molecular Surface Maps","authors":"Karsten Schatz, Florian Friess, M. Schäfer, T. Ertl, M. Krone","doi":"10.2312/vcbm.20201177","DOIUrl":"https://doi.org/10.2312/vcbm.20201177","url":null,"abstract":"Many biochemical and biomedical applications like protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We show that image similarity as found by our clustering corresponds to functional similarity of mapped proteins by comparing our results to the BRENDA database, which provides a hierarchical function-based annotation of enzymes. We also compare our results to the TM-score, which is a similarity value for pairs of arbitrary proteins. Our visualization prototype supports the entire workflow from map generation, similarity computing to clustering and can be used to interactively explore and analyze the results. CCS Concepts • Human-centered computing → Dendrograms; Scientific visualization; • Applied computing → Bioinformatics; © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association. DOI: 10.2312/vcbm.20201177 https://diglib.eg.org https://www.eg.org K. Schatz, F. Frieß, M. Schäfer, T. Ertl, and M. Krone / Analyzing Protein Similarity by Clustering Molecular Surface Maps","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"74 1","pages":"103-114"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72823269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

VRIDAA: Virtual Reality Platform for Training and Planning Implantations of Occluder Devices in Left Atrial Appendages VRIDAA:用于训练和规划左心房附件闭塞装置植入的虚拟现实平台

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2020-01-01 DOI: 10.2312/vcbm.20201168

E. Medina, Ainhoa M. Aguado, Jordi Mill, X. Freixa, D. Arzamendi, C. Yagüe, O. Camara

{"title":"VRIDAA: Virtual Reality Platform for Training and Planning Implantations of Occluder Devices in Left Atrial Appendages","authors":"E. Medina, Ainhoa M. Aguado, Jordi Mill, X. Freixa, D. Arzamendi, C. Yagüe, O. Camara","doi":"10.2312/vcbm.20201168","DOIUrl":"https://doi.org/10.2312/vcbm.20201168","url":null,"abstract":"Personalized anatomical information of the heart is usually obtained from the visual analysis of patient-specific medical images with standard multiplanar reconstruction (MPR) of 2D orthogonal slices, volume rendering and surface mesh views. Commonly, medical data is visualized in 2D flat screens, thus hampering the understanding of 3D complex anatomical details, including incorrect depth/scaling perception, which is critical for some cardiac interventions such as medical device implantations. Virtual reality (VR) is becoming a valid complementary technology overcoming some of the limitations of conventional visualization techniques and allowing an enhanced and fully interactive exploration of human anatomy. In this work, we present VRIDAA, a VR-based platform for the visualization of patient-specific cardiac geometries and the virtual implantation of left atrial appendage occluder (LAAO) devices. It includes different visualization and interaction modes to jointly inspect 3D LA geometries and different LAAO devices, MPR 2D imaging slices, several landmarks and morphological parameters relevant to LAAO, among other functionalities. The platform was designed and tested by two interventional cardiologists and LAAO researchers, obtaining very positive user feedback about its potential, highlighting VRIDAA as a source of motivation for trainees and its usefulness to better understand the required surgical approach before the intervention. CCS Concepts • Human-centered computing → Information visualization; • Applied computing → Interactive learning environments; Health care information systems;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"24 1","pages":"31-35"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73990793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

GLANCE: Visual Analytics for Monitoring Glaucoma Progression GLANCE:用于监测青光眼进展的可视化分析

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2020-01-01 DOI: 10.2312/vcbm.20201175

Astrid van den Brandt, Mark Christopher, L. Zangwill, Jasmin Rezapour, C. Bowd, Sally L. Baxter, D. Welsbie, A. Camp, S. Moghimi, Jiun L. Do, R. Weinreb, Chris C. P. Snijders, M. A. Westenberg

引用次数: 4

VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data virtualdsa++: ct血管造影数据的自动分割，血管标记，闭塞检测和图形搜索

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2020-01-01 DOI: 10.2312/vcbm.20201181

Florian Thamm, Markus Jürgens, H. Ditt, A. Maier

引用次数: 6

Visual Analysis of Multivariate Intensive Care Surveillance Data 多变量重症监护监测数据的可视化分析

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2020-01-01 DOI: 10.2312/vcbm.20201174

N. Brich, C. Schulz, Jörg Peter, Wilfried Klingert, M. Schenk, D. Weiskopf, M. Krone

{"title":"Visual Analysis of Multivariate Intensive Care Surveillance Data","authors":"N. Brich, C. Schulz, Jörg Peter, Wilfried Klingert, M. Schenk, D. Weiskopf, M. Krone","doi":"10.2312/vcbm.20201174","DOIUrl":"https://doi.org/10.2312/vcbm.20201174","url":null,"abstract":"We present an approach for visual analysis of high-dimensional measurement data with varying sampling rates in the context of an experimental post-surgery study performed on a porcine surrogate model. The study aimed at identifying parameters suitable for diagnosing and prognosticating the volume state—a crucial and difficult task in intensive care medicine. In intensive care, most assessments not only depend on a single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate time-dependent data remains a challenging task. We present a linked-view post hoc visual analysis application that reduces data complexity by combining projection-based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also the analysis of ensembles by adapting existing techniques using non-parametric statistics. We evaluated the effectiveness and acceptance of our application through expert feedback with domain scientists from the surgical department using real-world data: the results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition. Furthermore, the medical experts believe that our method can be transferred from medical research to the clinical context, for example, to identify the early onset of a sepsis. CCS Concepts • Applied computing → Health care information systems; • Mathematics of computing → Time series analysis; Dimensionality reduction; • Human-centered computing → Information visualization; © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association. DOI: 10.2312/vcbm.20201174 https://diglib.eg.org https://www.eg.org N. Brich et al. / Visual Analysis of Multivariate Intensive Care Surveillance Data","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"35 1","pages":"71-83"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82662318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Robustness Evaluation of CFD Simulations to Mesh Deformation 网格变形CFD仿真的鲁棒性评价

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2019-01-01 DOI: 10.2312/vcbm.20191244

Alexander Scheid-Rehder, K. Lawonn, M. Meuschke

引用次数: 1

Layer-Aware iOCT Volume Rendering for Retinal Surgery 视网膜手术的分层感知iOCT体积绘制

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2019-01-01 DOI: 10.2312/vcbm.20191239

J. Weiss, U. Eck, M. A. Nasseri, M. Maier, A. Eslami, N. Navab

引用次数: 6

Evolutionary Pathlines for Blood Flow Exploration in Cerebral Aneurysms 脑动脉瘤血流探测的进化途径

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2019-01-01 DOI: 10.2312/vcbm.20191250

B. Behrendt, W. Engelke, P. Berg, O. Beuing, B. Preim, I. Hotz, S. Saalfeld

{"title":"Evolutionary Pathlines for Blood Flow Exploration in Cerebral Aneurysms","authors":"B. Behrendt, W. Engelke, P. Berg, O. Beuing, B. Preim, I. Hotz, S. Saalfeld","doi":"10.2312/vcbm.20191250","DOIUrl":"https://doi.org/10.2312/vcbm.20191250","url":null,"abstract":"Blood flow simulations play an important role for the understanding of vascular diseases, such as aneurysms. However, analysis of the resulting flow patterns, especially comparisons across patient groups, are challenging. Typically, the hemodynamic analysis relies on trial and error inspection of the flow data based on pathline visualizations and surface renderings. Visualizing too many pathlines at once may obstruct interesting features, e.g., embedded vortices, whereas with too little pathlines, particularities such as flow characteristics in aneurysm blebs might be missed. While filtering and clustering techniques support this task, they require the pre-computation of pathlines densely sampled in the space-time domain. Not only does this become prohibitively expensive for large patient groups, but the results often suffer from undersampling artifacts. In this work, we propose the usage of evolutionary algorithms to reduce the overhead of computing pathlines that do not contribute to the analysis, while simultaneously reducing the undersampling artifacts. Integrated in an interactive framework, it efficiently supports the evaluation of hemodynamics for clinical research and treatment planning in case of cerebral aneurysms. The specification of general optimization criteria for entire patient groups allows the blood flow data to be batch-processed. We present clinical cases to demonstrate the benefits of our approach especially in presence of aneurysm blebs. Furthermore, we conducted an evaluation with four expert neuroradiologists. As a result, we report advantages of our method for treatment planning to underpin its clinical potential. CCS Concepts • Human-centered computing → Scientific visualization;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"57 1","pages":"253-263"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81479343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Visual Environment for Hypothesis Formation and Reasoning in Studies with fMRI and Multivariate Clinical Data 功能磁共振成像和多变量临床数据研究中假设形成和推理的视觉环境

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2019-01-01 DOI: 10.2312/vcbm.20191232

Daniel Jönsson, Albin Bergström, C. Forsell, Rozalyn Simon, M. Engström, A. Ynnerman, I. Hotz

引用次数: 9