N. Grossmann, O. Casares-Magaz, L. Muren, V. Moiseenko, J. Einck, E. Gröller, R. Raidou
In radiation therapy, anatomical changes in the patient might lead to deviations between the planned and delivered dose— including inadequate tumor coverage, and overradiation of healthy tissues. Exploring and analyzing anatomical changes throughout the entire treatment period can help clinical researchers to design appropriate treatment strategies, while identifying patients that are more prone to radiation-induced toxicity. We present the Pelvis Runner, a novel application for exploring the variability of segmented pelvic organs in multiple patients, across the entire radiation therapy treatment process. Our application addresses (i) the global exploration and analysis of pelvic organ shape variability in an abstracted tabular view and (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated. The workflow is based on available retrospective cohort data, which incorporate segmentations of the bladder, the prostate, and the rectum through the entire radiation therapy process. The Pelvis Runner is applied to four usage scenarios, which were conducted with two clinical researchers, i.e., medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment plan adaptation to anatomical changes. CCS Concepts • Human-centered computing → Visual analytics; • Applied computing → Life and medical sciences;
{"title":"Pelvis Runner: Visualizing Pelvic Organ Variability in a Cohort of Radiotherapy Patients","authors":"N. Grossmann, O. Casares-Magaz, L. Muren, V. Moiseenko, J. Einck, E. Gröller, R. Raidou","doi":"10.2312/vcbm.20191233","DOIUrl":"https://doi.org/10.2312/vcbm.20191233","url":null,"abstract":"In radiation therapy, anatomical changes in the patient might lead to deviations between the planned and delivered dose— including inadequate tumor coverage, and overradiation of healthy tissues. Exploring and analyzing anatomical changes throughout the entire treatment period can help clinical researchers to design appropriate treatment strategies, while identifying patients that are more prone to radiation-induced toxicity. We present the Pelvis Runner, a novel application for exploring the variability of segmented pelvic organs in multiple patients, across the entire radiation therapy treatment process. Our application addresses (i) the global exploration and analysis of pelvic organ shape variability in an abstracted tabular view and (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated. The workflow is based on available retrospective cohort data, which incorporate segmentations of the bladder, the prostate, and the rectum through the entire radiation therapy process. The Pelvis Runner is applied to four usage scenarios, which were conducted with two clinical researchers, i.e., medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment plan adaptation to anatomical changes. CCS Concepts • Human-centered computing → Visual analytics; • Applied computing → Life and medical sciences;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"6 1","pages":"69-78"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81805760","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}
H. Bartsch, L. Garrison, S. Bruckner, Ariel Wang, S. Tapert, R. Grüner
The RxNorm vocabulary is a yearly-published biomedical resource providing normalized names for me ications. It is used to capture medication use in the Adolescent Brain Cognitive Development (ABCD) study, an active and publicly available longitudinal research study following 11,800 children over 10 years. In this work, we present medUse, a visual tool allowing researchers to explore and analyze the relationship of drug category to cognitive or imaging derived measures using ABCD study data. Our tool provides position-based context for tree traversal and sele tion granularity of both st y participants and drug category. Developed as part of the Data Exploration and Analysis Portal (DEAP), medUse is available to more than 600 ABCD researchers world-wide. By integrating medUse into an actively used research product we are able to reach a wide audience and increase the practical relevance of visualization for the biomedical field. CCS Concepts • Human-centered computing → Information visualization; Activity centered design;
{"title":"MedUse: A Visual Analysis Tool for Medication Use Data in the ABCD Study","authors":"H. Bartsch, L. Garrison, S. Bruckner, Ariel Wang, S. Tapert, R. Grüner","doi":"10.2312/vcbm.20191236","DOIUrl":"https://doi.org/10.2312/vcbm.20191236","url":null,"abstract":"The RxNorm vocabulary is a yearly-published biomedical resource providing normalized names for me ications. It is used to capture medication use in the Adolescent Brain Cognitive Development (ABCD) study, an active and publicly available longitudinal research study following 11,800 children over 10 years. In this work, we present medUse, a visual tool allowing researchers to explore and analyze the relationship of drug category to cognitive or imaging derived measures using ABCD study data. Our tool provides position-based context for tree traversal and sele tion granularity of both st y participants and drug category. Developed as part of the Data Exploration and Analysis Portal (DEAP), medUse is available to more than 600 ABCD researchers world-wide. By integrating medUse into an actively used research product we are able to reach a wide audience and increase the practical relevance of visualization for the biomedical field. CCS Concepts • Human-centered computing → Information visualization; Activity centered design;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"31 1","pages":"97-101"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74480618","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}
N. Lichtenberg, Bastian Krayer, C. Hansen, S. Müller, K. Lawonn
In this paper, we make contributions to the visualization of vascular structures. Based on skeletal input data, we provide a combined 2D and implicit 3D visualization of vasculature, that is parameterized on-the-fly for illustrative visualization. We use an efficient algorithm that creates a distance field volume from triangles and extend it to handle skeletal tree data. Spheretracing this volume allows to visualize the vasculature in a flexible way, without the need to recompute the volume. Illustrative techniques, that have been frequently applied to vascular visualizations often require texture coordinates. Therefore, modifying an object-based algorithm, we propose an image-based, hierarchical optimization process that allows to derive periodic texture coordinates in a frame-coherent way and suits the implicit representation of the vascular structures. In addition to the 3D surface visualization, we propose a simple layout algorithm that applies a 2D parameterization to the skeletal tree nodes. This parameterization can be used to color-code the vasculature or to plot a 2D overview-graph, that highlights the branching topology of the skeleton. We transfer measurements, done in 3D space, to the 2D plot in order to avoid visual clutter and self occlusions in the 3D representation. A visual link between the 3D and 2D views is established via color codes and texture patterns. The potential of our pipeline is shown in several prototypical application scenarios.
{"title":"Distance Field Visualization and 2D Abstraction of Vessel Tree Structures with on-the-fly Parameterization","authors":"N. Lichtenberg, Bastian Krayer, C. Hansen, S. Müller, K. Lawonn","doi":"10.2312/vcbm.20191251","DOIUrl":"https://doi.org/10.2312/vcbm.20191251","url":null,"abstract":"In this paper, we make contributions to the visualization of vascular structures. Based on skeletal input data, we provide a combined 2D and implicit 3D visualization of vasculature, that is parameterized on-the-fly for illustrative visualization. We use an efficient algorithm that creates a distance field volume from triangles and extend it to handle skeletal tree data. Spheretracing this volume allows to visualize the vasculature in a flexible way, without the need to recompute the volume. Illustrative techniques, that have been frequently applied to vascular visualizations often require texture coordinates. Therefore, modifying an object-based algorithm, we propose an image-based, hierarchical optimization process that allows to derive periodic texture coordinates in a frame-coherent way and suits the implicit representation of the vascular structures. In addition to the 3D surface visualization, we propose a simple layout algorithm that applies a 2D parameterization to the skeletal tree nodes. This parameterization can be used to color-code the vasculature or to plot a 2D overview-graph, that highlights the branching topology of the skeleton. We transfer measurements, done in 3D space, to the 2D plot in order to avoid visual clutter and self occlusions in the 3D representation. A visual link between the 3D and 2D views is established via color codes and texture patterns. The potential of our pipeline is shown in several prototypical application scenarios.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"63 1","pages":"265-278"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86136627","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}
S. Alemzadeh, F. Kromp, B. Preim, S. Taschner-Mandl, K. Bühler
We introduce discoVA as a visual analytics tool for the refinement of risk stratification of cancer patients and biomarker discovery. Currently, tools for the joint analysis of multiple biological and clinical information in this field are insufficient or lacking. Our tool fills this gap by enabling bio-medical experts to explore datasets of cancer patient cohorts. By using multiple coordinated visualization techniques, nested visual queries on various data types can be performed to generate/prove a hypothesis by identifying discrete sub-cohorts. We demonstrated the utility of discoVA by a case study involving bio-medical researchers.
{"title":"A Visual Analytics Approach for Patient Stratification and Biomarker Discovery","authors":"S. Alemzadeh, F. Kromp, B. Preim, S. Taschner-Mandl, K. Bühler","doi":"10.2312/vcbm.20191235","DOIUrl":"https://doi.org/10.2312/vcbm.20191235","url":null,"abstract":"We introduce discoVA as a visual analytics tool for the refinement of risk stratification of cancer patients and biomarker discovery. Currently, tools for the joint analysis of multiple biological and clinical information in this field are insufficient or lacking. Our tool fills this gap by enabling bio-medical experts to explore datasets of cancer patient cohorts. By using multiple coordinated visualization techniques, nested visual queries on various data types can be performed to generate/prove a hypothesis by identifying discrete sub-cohorts. We demonstrated the utility of discoVA by a case study involving bio-medical researchers.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"377 1","pages":"91-95"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80602869","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}
Niels H. L. C. de Hoon, K. Lawonn, A. Jalba, E. Eisemann, A. Vilanova
Phase-Contrast Magnetic Resonance Imaging (PC-MRI) measures volumetric and time-varying blood flow data, unsurpassed in quality and completeness. Such blood-flow data have been shown to have the potential to improve both diagnosis and risk assessment of cardiovascular diseases (CVDs) uniquely. Typically PC-MRI data is visualized using stream- or pathlines. However, time-varying aspects of the data, e.g., vortex shedding, breakdown, and formation, are not sufficiently captured by these visualization techniques. Experimental flow visualization techniques introduce a visible medium, like smoke or dye, to visualize flow aspects including time-varying aspects. We propose a framework that mimics such experimental techniques by using a high number of particles. The framework offers great flexibility which allows for various visualization approaches. These include common traditional flow visualizations, but also streak visualizations to show the temporal aspects, and uncertainty visualizations. Moreover, these patient-specific measurements suffer from noise artifacts and a coarse resolution, causing uncertainty. Traditional flow visualizations neglect uncertainty and, therefore, may give a false sense of certainty, which can mislead the user yielding incorrect decisions. Previously, the domain experts had no means to visualize the effect of the uncertainty in the data. Our framework has been adopted by domain experts to visualize the vortices present in the sinuses of the aorta root showing the potential of the framework. Furthermore, an evaluation among domain experts indicated that having the option to visualize the uncertainty contributed to their confidence on the analysis.
{"title":"InkVis: A High-Particle-Count Approach for Visualization of Phase-Contrast Magnetic Resonance Imaging Data","authors":"Niels H. L. C. de Hoon, K. Lawonn, A. Jalba, E. Eisemann, A. Vilanova","doi":"10.2312/VCBM.20191243","DOIUrl":"https://doi.org/10.2312/VCBM.20191243","url":null,"abstract":"Phase-Contrast Magnetic Resonance Imaging (PC-MRI) measures volumetric and time-varying blood flow data, unsurpassed in quality and completeness. Such blood-flow data have been shown to have the potential to improve both diagnosis and risk assessment of cardiovascular diseases (CVDs) uniquely. Typically PC-MRI data is visualized using stream- or pathlines. However, time-varying aspects of the data, e.g., vortex shedding, breakdown, and formation, are not sufficiently captured by these visualization techniques. Experimental flow visualization techniques introduce a visible medium, like smoke or dye, to visualize flow aspects including time-varying aspects. We propose a framework that mimics such experimental techniques by using a high number of particles. The framework offers great flexibility which allows for various visualization approaches. These include common traditional flow visualizations, but also streak visualizations to show the temporal aspects, and uncertainty visualizations. Moreover, these patient-specific measurements suffer from noise artifacts and a coarse resolution, causing uncertainty. Traditional flow visualizations neglect uncertainty and, therefore, may give a false sense of certainty, which can mislead the user yielding incorrect decisions. Previously, the domain experts had no means to visualize the effect of the uncertainty in the data. Our framework has been adopted by domain experts to visualize the vortices present in the sinuses of the aorta root showing the potential of the framework. Furthermore, an evaluation among domain experts indicated that having the option to visualize the uncertainty contributed to their confidence on the analysis.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"31 1","pages":"177-188"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85562886","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}
Brain MR images are one of the most important instruments for diagnosing neurological disorders such as tumors, infections or trauma. In particular, grade I-IV brain tumors are a well-studied subject for supervised deep learning approaches. However, for a clinical use of these approaches, a very large annotated database that covers all of the occurring variance is necessary. As MR scanners are not quantitative, it is unclear how good supervised approaches, trained on a specific database, will actually perform on a new set of images that may stem from a yet other scanner. We propose a new method for brain tumor segmentation, that can not only identify abnormal regions, but can also delineate brain tumors into three characteristic radiological areas: The edema, the enhancing core, and the non-enhancing and necrotic tissue. Our concept is based on FLAIR and T1CE MRI sequences, where abnormalities are detected with a variational autoencoder trained on healthy examples. The detected areas are finally postprocessed via Gaussian Mixture Models and finally classified according to the three defined labels. We show results on the BraTS2018 dataset and compare these to previously published unsupervised segmentation results as well as to the results of the BraTS challenge 2018. Our developed unsupervised anomaly detection approach is on par with previously published methods. Meanwhile, the semantic segmentation a new and unique model shows encouraging results.
{"title":"Semantic Segmentation of Brain Tumors in MRI Data Without any Labels","authors":"Leon Weninger, Imke Krauhausen, D. Merhof","doi":"10.2312/vcbm.20191230","DOIUrl":"https://doi.org/10.2312/vcbm.20191230","url":null,"abstract":"Brain MR images are one of the most important instruments for diagnosing neurological disorders such as tumors, infections or trauma. In particular, grade I-IV brain tumors are a well-studied subject for supervised deep learning approaches. However, for a clinical use of these approaches, a very large annotated database that covers all of the occurring variance is necessary. As MR scanners are not quantitative, it is unclear how good supervised approaches, trained on a specific database, will actually perform on a new set of images that may stem from a yet other scanner. We propose a new method for brain tumor segmentation, that can not only identify abnormal regions, but can also delineate brain tumors into three characteristic radiological areas: The edema, the enhancing core, and the non-enhancing and necrotic tissue. Our concept is based on FLAIR and T1CE MRI sequences, where abnormalities are detected with a variational autoencoder trained on healthy examples. The detected areas are finally postprocessed via Gaussian Mixture Models and finally classified according to the three defined labels. We show results on the BraTS2018 dataset and compare these to previously published unsupervised segmentation results as well as to the results of the BraTS challenge 2018. Our developed unsupervised anomaly detection approach is on par with previously published methods. Meanwhile, the semantic segmentation a new and unique model shows encouraging results.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"80 1","pages":"45-49"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84120189","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}
Animation is a potentially powerful instrument to convey complex information with movements, smooth transitions between different states that employ the strong human capabilities to perceive and interpret motion. Animation is a natural choice to display time-dependent data where the dynamic nature of the data is mapped to a kind of video (temporal animation). Clipping planes may be smoothly translated and object transparency adapted to control visibility and further support emphasis of spatial relations, e.g. around a tumor. Animation, however, may also be employed for static data, e.g. to move a camera along a predefined path to convey complex anatomical structures. Virtual endoscopy, where the virtual camera is moved inside an air-filled or fluid-filled structure is a prominent example for these non-temporal animations. Animations, however, are complex visualizations that may depict a larger number of changes in a short period of time. Thus, they need to be assessed in their capability to actually convey information. In this paper, we give a survey of temporal and non-temporal animated visualizations focussed on medical applications and discuss the research potential that arises. To be employed more widely, cognitive limitations, e.g. change blindness, need to be considered. The reduction of complexity in temporal animations is an essential topic to enable the detection and interpretation of changes. Emphasis techniques may guide the user’s attention and improve the perception of essential features. Finally, interaction beyond the typical video recorder functionality is considered. Although our focus is medicine, the discussion of a research agenda is partially based on cartography, where animation is widely used.
{"title":"Medical Animations: A Survey and a Research Agenda","authors":"B. Preim, M. Meuschke","doi":"10.2312/vcbm.20191241","DOIUrl":"https://doi.org/10.2312/vcbm.20191241","url":null,"abstract":"Animation is a potentially powerful instrument to convey complex information with movements, smooth transitions between different states that employ the strong human capabilities to perceive and interpret motion. Animation is a natural choice to display time-dependent data where the dynamic nature of the data is mapped to a kind of video (temporal animation). Clipping planes may be smoothly translated and object transparency adapted to control visibility and further support emphasis of spatial relations, e.g. around a tumor. Animation, however, may also be employed for static data, e.g. to move a camera along a predefined path to convey complex anatomical structures. Virtual endoscopy, where the virtual camera is moved inside an air-filled or fluid-filled structure is a prominent example for these non-temporal animations. Animations, however, are complex visualizations that may depict a larger number of changes in a short period of time. Thus, they need to be assessed in their capability to actually convey information. In this paper, we give a survey of temporal and non-temporal animated visualizations focussed on medical applications and discuss the research potential that arises. To be employed more widely, cognitive limitations, e.g. change blindness, need to be considered. The reduction of complexity in temporal animations is an essential topic to enable the detection and interpretation of changes. Emphasis techniques may guide the user’s attention and improve the perception of essential features. Finally, interaction beyond the typical video recorder functionality is considered. Although our focus is medicine, the discussion of a research agenda is partially based on cartography, where animation is widely used.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"57 1","pages":"145-164"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76928826","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}
Víctor Ceballos, E. Monclús, Pere-Pau Vázquez, Álvaro Bendezú, M. Mego, X. Merino, F. Azpiroz, I. Navazo
The analysis of the morphology and content of the gut is necessary in order to achieve a better understanding of its metabolicand functional activity. Magnetic resonance imaging (MRI) has become an important imaging technique since it is able tovisualize soft tissues in an undisturbed bowel using no ionizing radiation.In the last few years, MRI of gastrointestinal function has advanced substantially. However, few studies have focused on thecolon, because the analysis of colonic content is time consuming and cumbersome.This paper presents a semi-automatic segmentation tool for the quantitative assessment of the unprepared colon from MRIimages. The techniques developed here have been crucial for a number of clinical experiments.
{"title":"Colonic Content Assessment from MRI Imaging Using a Semi-automatic Approach","authors":"Víctor Ceballos, E. Monclús, Pere-Pau Vázquez, Álvaro Bendezú, M. Mego, X. Merino, F. Azpiroz, I. Navazo","doi":"10.2312/VCBM.20191227","DOIUrl":"https://doi.org/10.2312/VCBM.20191227","url":null,"abstract":"The analysis of the morphology and content of the gut is necessary in order to achieve a better understanding of its metabolicand functional activity. Magnetic resonance imaging (MRI) has become an important imaging technique since it is able tovisualize soft tissues in an undisturbed bowel using no ionizing radiation.In the last few years, MRI of gastrointestinal function has advanced substantially. However, few studies have focused on thecolon, because the analysis of colonic content is time consuming and cumbersome.This paper presents a semi-automatic segmentation tool for the quantitative assessment of the unprepared colon from MRIimages. The techniques developed here have been crucial for a number of clinical experiments.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"42 1","pages":"17-26"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75314747","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}
M. Sbardellati, H. Miao, Hsiang-Yun Wu, E. Gröller, I. Barišić, I. Viola
We propose an approach to interactively create exploded views of molecular structures with the goal to help domain experts in their design process and provide them with a meaningful visual representation of component relationships. Exploded views are excellently suited to manage visual occlusion of structure components, which is one of the main challenges when visualizing complex 3D data. In this paper, we discuss four key parameters of an exploded view: explosion distance, direction, order, and the selection of explosion components. We propose two strategies, namely the structure-derived exploded view and the interactive free-form exploded view, for computing these four parameters systematically. The first strategy allows scientists to automatically create exploded views by computing the parameters from the given object structures. The second strategy further supports them to design and customize detailed explosion paths through user interaction. Our approach features the possibility to animate exploded views, to incorporate ease functions into these animations and to display the explosion path of components via arrows. Finally, we demonstrate three use cases with various challenges that we investigated in collaboration with a domain scientist. Our approach, therefore, provides interesting new ways of investigating and presenting the design layout and composition of complex molecular structures. CCS Concepts • Human-centered computing → Scientific visualization; Visualization toolkits;
{"title":"Interactive Exploded Views for Molecular Structures","authors":"M. Sbardellati, H. Miao, Hsiang-Yun Wu, E. Gröller, I. Barišić, I. Viola","doi":"10.2312/vcbm.20191237","DOIUrl":"https://doi.org/10.2312/vcbm.20191237","url":null,"abstract":"We propose an approach to interactively create exploded views of molecular structures with the goal to help domain experts in their design process and provide them with a meaningful visual representation of component relationships. Exploded views are excellently suited to manage visual occlusion of structure components, which is one of the main challenges when visualizing complex 3D data. In this paper, we discuss four key parameters of an exploded view: explosion distance, direction, order, and the selection of explosion components. We propose two strategies, namely the structure-derived exploded view and the interactive free-form exploded view, for computing these four parameters systematically. The first strategy allows scientists to automatically create exploded views by computing the parameters from the given object structures. The second strategy further supports them to design and customize detailed explosion paths through user interaction. Our approach features the possibility to animate exploded views, to incorporate ease functions into these animations and to display the explosion path of components via arrows. Finally, we demonstrate three use cases with various challenges that we investigated in collaboration with a domain scientist. Our approach, therefore, provides interesting new ways of investigating and presenting the design layout and composition of complex molecular structures. CCS Concepts • Human-centered computing → Scientific visualization; Visualization toolkits;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"309 1","pages":"103-112"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81235607","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}
This design study paper describes preha, a novel visual analytics application in the field of in-patient rehabilitation. We conducted extensive interviews with the intended users, i.e., engineers and clinical rehabilitation experts, to determine specific requirements of their analytical process. We identified nine tasks, for which suitable solutions have been designed and developed in the flexible environment of kibana. Our application is used to analyze existing rehabilitation data from a large cohort of 46,000 patients, and it is the first integrated solution of its kind. It incorporates functionalities for data preprocessing (profiling, wrangling and cleansing), storage, visualization, and predictive analysis on the basis of retrospective outcomes. A positive feedback from the first evaluation with domain experts indicates the usefulness of the newly proposed approach and represents a solid foundation for the introduction of visual analytics to the rehabilitation domain. CCS Concepts • Human-centered computing → Visual analytics; • Applied computing → Life and medical sciences;
{"title":"preha: Establishing Precision Rehabilitation with Visual Analytics","authors":"Georg Bernold, K. Matkovič, E. Gröller, R. Raidou","doi":"10.2312/vcbm.20191234","DOIUrl":"https://doi.org/10.2312/vcbm.20191234","url":null,"abstract":"This design study paper describes preha, a novel visual analytics application in the field of in-patient rehabilitation. We conducted extensive interviews with the intended users, i.e., engineers and clinical rehabilitation experts, to determine specific requirements of their analytical process. We identified nine tasks, for which suitable solutions have been designed and developed in the flexible environment of kibana. Our application is used to analyze existing rehabilitation data from a large cohort of 46,000 patients, and it is the first integrated solution of its kind. It incorporates functionalities for data preprocessing (profiling, wrangling and cleansing), storage, visualization, and predictive analysis on the basis of retrospective outcomes. A positive feedback from the first evaluation with domain experts indicates the usefulness of the newly proposed approach and represents a solid foundation for the introduction of visual analytics to the rehabilitation domain. CCS Concepts • Human-centered computing → Visual analytics; • Applied computing → Life and medical sciences;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"90 1","pages":"79-89"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74537609","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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