M. Falk, P. Ljung, C. Lundström, A. Ynnerman, I. Hotz
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;
{"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}
E. Medina, Ainhoa M. Aguado, Jordi Mill, X. Freixa, D. Arzamendi, C. Yagüe, O. Camara
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;
{"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}
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
{"title":"GLANCE: Visual Analytics for Monitoring Glaucoma Progression","authors":"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","doi":"10.2312/vcbm.20201175","DOIUrl":"https://doi.org/10.2312/vcbm.20201175","url":null,"abstract":"","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"33 19 1","pages":"85-96"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82553066","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}
{"title":"VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data","authors":"Florian Thamm, Markus Jürgens, H. Ditt, A. Maier","doi":"10.2312/vcbm.20201181","DOIUrl":"https://doi.org/10.2312/vcbm.20201181","url":null,"abstract":"","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"3 1","pages":"151-155"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89249032","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}
CFD simulations are an increasingly important method for the non-invasive analysis of risk factors for aneurysm rupture. Their robustness, however, has to be examined more thoroughly before clinical use is possible. We present a novel framework that enables robustness evaluation of CFD simulation according to mesh deformation on patient-specific blood vessel geometry. Our tool offers a guided workflow to generate, run, and visualize OpenFOAM simulations, which significantly decreases the usual overhead of CFD simulations with OpenFOAM. Besides, the deformation of the original geometry allows the user to evaluate the robustness of the simulation without the need to repeat expensive operations of the data pre-processing phase. We assessed the robustness of CFD simulations by applying our framework to several aneurysm data sets. CCS Concepts • Human-centered computing → Scientific visualization;
{"title":"Robustness Evaluation of CFD Simulations to Mesh Deformation","authors":"Alexander Scheid-Rehder, K. Lawonn, M. Meuschke","doi":"10.2312/vcbm.20191244","DOIUrl":"https://doi.org/10.2312/vcbm.20191244","url":null,"abstract":"CFD simulations are an increasingly important method for the non-invasive analysis of risk factors for aneurysm rupture. Their robustness, however, has to be examined more thoroughly before clinical use is possible. We present a novel framework that enables robustness evaluation of CFD simulation according to mesh deformation on patient-specific blood vessel geometry. Our tool offers a guided workflow to generate, run, and visualize OpenFOAM simulations, which significantly decreases the usual overhead of CFD simulations with OpenFOAM. Besides, the deformation of the original geometry allows the user to evaluate the robustness of the simulation without the need to repeat expensive operations of the data pre-processing phase. We assessed the robustness of CFD simulations by applying our framework to several aneurysm data sets. CCS Concepts • Human-centered computing → Scientific visualization;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"44 5 1","pages":"189-199"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84071539","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}
J. Weiss, U. Eck, M. A. Nasseri, M. Maier, A. Eslami, N. Navab
{"title":"Layer-Aware iOCT Volume Rendering for Retinal Surgery","authors":"J. Weiss, U. Eck, M. A. Nasseri, M. Maier, A. Eslami, N. Navab","doi":"10.2312/vcbm.20191239","DOIUrl":"https://doi.org/10.2312/vcbm.20191239","url":null,"abstract":"","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"52 1","pages":"123-127"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86873999","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}
B. Behrendt, W. Engelke, P. Berg, O. Beuing, B. Preim, I. Hotz, S. Saalfeld
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;
{"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}
Daniel Jönsson, Albin Bergström, C. Forsell, Rozalyn Simon, M. Engström, A. Ynnerman, I. Hotz
We present an interactive visual environment for linked analysis of brain imaging and clinical measurements. The environment is developed in an iterative participatory design process involving neur ...
{"title":"A Visual Environment for Hypothesis Formation and Reasoning in Studies with fMRI and Multivariate Clinical Data","authors":"Daniel Jönsson, Albin Bergström, C. Forsell, Rozalyn Simon, M. Engström, A. Ynnerman, I. Hotz","doi":"10.2312/vcbm.20191232","DOIUrl":"https://doi.org/10.2312/vcbm.20191232","url":null,"abstract":"We present an interactive visual environment for linked analysis of brain imaging and clinical measurements. The environment is developed in an iterative participatory design process involving neur ...","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"46 1","pages":"57-68"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77682283","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}
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