H. S. G. Caballero, A. Corvó, F. B. Meulen, P. Fonseca, S. Overeem, J. V. Wijk, M. A. Westenberg
Machine learning is becoming increasingly popular in the medical domain. In the near future, clinicians expect predictive models to support daily tasks such as diagnosis and prognostic analysis. For this reason, it is utterly important to evaluate and compare the performance of such models so that clinicians can safely rely on them. In this paper, we focus on sleep staging wherein machine learning models can be used to automate or support sleep scoring. Evaluation of these models is complex because sleep is a natural process, which varies among patients. For adoption in clinical routine, it is important to understand how the models perform for different groups of patients. Moreover, models can be trained to recognize different characteristics in the data, and model developers need to understand why and how performance of the different models varies. To address these challenges, we present a visual analytics approach to evaluate the performance of predictive models on sleep staging and to help experts better understand these models with respect to patient data (e.g., conditions, medication, etc.). We illustrate the effectiveness of our approach by comparing multiple models trained on real-world sleep staging data with experts.
{"title":"PerSleep: A Visual Analytics Approach for Performance Assessment of Sleep Staging Models","authors":"H. S. G. Caballero, A. Corvó, F. B. Meulen, P. Fonseca, S. Overeem, J. V. Wijk, M. A. Westenberg","doi":"10.2312/vcbm.20211352","DOIUrl":"https://doi.org/10.2312/vcbm.20211352","url":null,"abstract":"Machine learning is becoming increasingly popular in the medical domain. In the near future, clinicians expect predictive models to support daily tasks such as diagnosis and prognostic analysis. For this reason, it is utterly important to evaluate and compare the performance of such models so that clinicians can safely rely on them. In this paper, we focus on sleep staging wherein machine learning models can be used to automate or support sleep scoring. Evaluation of these models is complex because sleep is a natural process, which varies among patients. For adoption in clinical routine, it is important to understand how the models perform for different groups of patients. Moreover, models can be trained to recognize different characteristics in the data, and model developers need to understand why and how performance of the different models varies. To address these challenges, we present a visual analytics approach to evaluate the performance of predictive models on sleep staging and to help experts better understand these models with respect to patient data (e.g., conditions, medication, etc.). We illustrate the effectiveness of our approach by comparing multiple models trained on real-world sleep staging data with experts.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"78 1","pages":"123-133"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74590307","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}
Real-world sculptures that display patient imaging data for anatomical education purposes have seen a recent resurgence through the field of data physicalization . In this paper, we describe an automated process for the computer-assisted generation of sculptures that can be employed for anatomical education among the general population. We propose a workflow that supports non-expert users to generate and physically display volumetric medical data in a visually appealing and engaging way. Our approach generates slide-based, interactive sculptures—called volograms —that resemble holograms of underlying medical data. The volograms are made out of affordable and readily available materials (e.g., transparent foils and cardboard) and can be produced through commonly available means. To evaluate the educational value of the proposed approach with our target audience, we assess the volograms, as opposed to classical, on-screen medical visualizations in a user study. The results of our study, while highlighting current weaknesses of our physicalization, also point to interesting future directions.
{"title":"Vologram: An Educational Holographic Sculpture for Volumetric Medical Data Physicalization","authors":"Daniel Pahr, Hsiang-Yun Wu, R. Raidou","doi":"10.2312/vcbm.20211341","DOIUrl":"https://doi.org/10.2312/vcbm.20211341","url":null,"abstract":"Real-world sculptures that display patient imaging data for anatomical education purposes have seen a recent resurgence through the field of data physicalization . In this paper, we describe an automated process for the computer-assisted generation of sculptures that can be employed for anatomical education among the general population. We propose a workflow that supports non-expert users to generate and physically display volumetric medical data in a visually appealing and engaging way. Our approach generates slide-based, interactive sculptures—called volograms —that resemble holograms of underlying medical data. The volograms are made out of affordable and readily available materials (e.g., transparent foils and cardboard) and can be produced through commonly available means. To evaluate the educational value of the proposed approach with our target audience, we assess the volograms, as opposed to classical, on-screen medical visualizations in a user study. The results of our study, while highlighting current weaknesses of our physicalization, also point to interesting future directions.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"16 1","pages":"19-23"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91216027","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}
4D Flow MRI is a recent promising technology that is able to capture blood flow information within the heart chambers over a cardiac cycle. To accurately study the flow inside the chambers, there is a need for a high quality anatomical reference which can be provided by another scan known as 3D cine MRI (short-axis 3D (multiple 2D slices) cine SSFP). To take advantage of both scans, data fusion can be done using an intensity-based registration. To reduce the impact of noise on the registration result and the chance of misalignment between the organs, defining a region of interest (localization) should be done prior to the registration. Localizing a dataset – especially a time-varying dataset – can be a daunting task since the localization should be provided for all time frames. We design and evaluate different strategies for extending single time frame localization to time varying data in order to register the 4D Flow MRI and 3D cine MRI over the cardiac cycle. CCS Concepts • Applied computing → Life and medical sciences;
{"title":"Strategies for Generating Multi-Time Frame Localization of Cardiac MRI","authors":"Samin Sabokrohiyeh, Kathleen Ang, F. Samavati","doi":"10.2312/vcbm.20211342","DOIUrl":"https://doi.org/10.2312/vcbm.20211342","url":null,"abstract":"4D Flow MRI is a recent promising technology that is able to capture blood flow information within the heart chambers over a cardiac cycle. To accurately study the flow inside the chambers, there is a need for a high quality anatomical reference which can be provided by another scan known as 3D cine MRI (short-axis 3D (multiple 2D slices) cine SSFP). To take advantage of both scans, data fusion can be done using an intensity-based registration. To reduce the impact of noise on the registration result and the chance of misalignment between the organs, defining a region of interest (localization) should be done prior to the registration. Localizing a dataset – especially a time-varying dataset – can be a daunting task since the localization should be provided for all time frames. We design and evaluate different strategies for extending single time frame localization to time varying data in order to register the 4D Flow MRI and 3D cine MRI over the cardiac cycle. CCS Concepts • Applied computing → Life and medical sciences;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"25-29"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76656003","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}
P. Eulzer, K. Richter, M. Meuschke, A. Hundertmark, K. Lawonn
We propose a novel method to cut and flatten vascular geometry that results in an intuitive mapping between the 3D and 2D domains. Our approach is fully automatic, and the sole input is the vessel geometry. We aim to simplify parameter analysis on vessel walls for research on vascular disease and computational hemodynamics. We present a use case for the flattening to aid efforts in investigating the pathophysiology of carotid stenoses (vessel constrictions that are a root cause of stroke). To achieve an intuitive mapping, we introduce the notion of natural vessel cuts. They remain on one side of vessel branches, meaning they adhere to the longitudinal direction and thus result in a comprehensible unfolding of the geometry. Vessel branches and endpoints are automatically detected, and a 2D layout configuration is found that retains the original branch layout. We employ a quasi-isometric surface parameterization to map the geometry to the 2D domain as a single patch. The flattened depiction resolves the need for tedious 3D interaction as the whole surface is visible at once. We found this overview particularly beneficial for exploring temporally resolved parameters.
{"title":"Automatic Cutting and Flattening of Carotid Artery Geometries","authors":"P. Eulzer, K. Richter, M. Meuschke, A. Hundertmark, K. Lawonn","doi":"10.2312/vcbm.20211347","DOIUrl":"https://doi.org/10.2312/vcbm.20211347","url":null,"abstract":"We propose a novel method to cut and flatten vascular geometry that results in an intuitive mapping between the 3D and 2D domains. Our approach is fully automatic, and the sole input is the vessel geometry. We aim to simplify parameter analysis on vessel walls for research on vascular disease and computational hemodynamics. We present a use case for the flattening to aid efforts in investigating the pathophysiology of carotid stenoses (vessel constrictions that are a root cause of stroke). To achieve an intuitive mapping, we introduce the notion of natural vessel cuts. They remain on one side of vessel branches, meaning they adhere to the longitudinal direction and thus result in a comprehensible unfolding of the geometry. Vessel branches and endpoints are automatically detected, and a 2D layout configuration is found that retains the original branch layout. We employ a quasi-isometric surface parameterization to map the geometry to the 2D domain as a single patch. The flattened depiction resolves the need for tedious 3D interaction as the whole surface is visible at once. We found this overview particularly beneficial for exploring temporally resolved parameters.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"79-89"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86516001","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}
Sherin Sugathan, H. Bartsch, F. Riemer, R. Grüner, K. Lawonn, N. Smit
Multiple Sclerosis (MS) is a brain disease that is diagnosed and monitored extensively through MRI scans. One of the criteria is the appearance of so-called brain lesions. The lesions show up on MRI scans as regions with elevated or reduced contrast compared to the surrounding healthy tissue. Understanding the complex interplay of contrast, location and shape in images from multiple modalities from 2D MRI slices is challenging. Advanced visualization of appearanceand location-related features of lesions would help researchers in defining better disease characterization through MS research. Since a permanent cure is not possible in MS and medication-based disease modification is a common treatment path, providing better visualizations would strengthen research which investigates the effect of white matter lesions. Here we present an advanced visualization solution that supports analysis from multiple imaging modalities acquired in a clinical routine examination. The solution holds potential for enabling researchers to have a more intuitive perception of lesion features. As an example for enhancing the analytic possibilities, we demonstrate the benefits of lesion projection using both Diffusion Tensor Imaging (DTI) and gradient-based techniques. This approach enables users to assess brain structures across individuals as the atlas-based analysis provides 3D anchoring and labeling of regions across a series of brain scans from the same participant and across different participants. The projections on the brain surface also enable researchers to conduct detailed studies on the relationship between cognitive disabilities and location of lesions. This allows researchers to correlate lesions to Brodmann areas and related brain functions. We realize the solutions in a prototype application that supports both DTI and structural data. A qualitative evaluation demonstrates that our approach supports MS researchers by providing new opportunities for MS research. CCS Concepts • Human-centered computing → Visualization application domains;
{"title":"Interactive Multimodal Imaging Visualization for Multiple Sclerosis Lesion Analysis","authors":"Sherin Sugathan, H. Bartsch, F. Riemer, R. Grüner, K. Lawonn, N. Smit","doi":"10.2312/vcbm.20211346","DOIUrl":"https://doi.org/10.2312/vcbm.20211346","url":null,"abstract":"Multiple Sclerosis (MS) is a brain disease that is diagnosed and monitored extensively through MRI scans. One of the criteria is the appearance of so-called brain lesions. The lesions show up on MRI scans as regions with elevated or reduced contrast compared to the surrounding healthy tissue. Understanding the complex interplay of contrast, location and shape in images from multiple modalities from 2D MRI slices is challenging. Advanced visualization of appearanceand location-related features of lesions would help researchers in defining better disease characterization through MS research. Since a permanent cure is not possible in MS and medication-based disease modification is a common treatment path, providing better visualizations would strengthen research which investigates the effect of white matter lesions. Here we present an advanced visualization solution that supports analysis from multiple imaging modalities acquired in a clinical routine examination. The solution holds potential for enabling researchers to have a more intuitive perception of lesion features. As an example for enhancing the analytic possibilities, we demonstrate the benefits of lesion projection using both Diffusion Tensor Imaging (DTI) and gradient-based techniques. This approach enables users to assess brain structures across individuals as the atlas-based analysis provides 3D anchoring and labeling of regions across a series of brain scans from the same participant and across different participants. The projections on the brain surface also enable researchers to conduct detailed studies on the relationship between cognitive disabilities and location of lesions. This allows researchers to correlate lesions to Brodmann areas and related brain functions. We realize the solutions in a prototype application that supports both DTI and structural data. A qualitative evaluation demonstrates that our approach supports MS researchers by providing new opportunities for MS research. CCS Concepts • Human-centered computing → Visualization application domains;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"123 1","pages":"65-77"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79481672","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}
Vikram Apilla, B. Behrendt, K. Lawonn, B. Preim, M. Meuschke
We present an approach for computing camera animations composed of optimal views to support the visual exploration of blood flow data using cerebral aneurysms as major example. Medical researchers are interested in hemodynamic parameters and vessel wall characteristics. The time-dependent character of blood flow data complicates the visual analysis. Our approach is modeled as an optimization problem to automatically determine camera paths during the cardiac cycle. These consist of optimal viewpoints showing regions with suspicious characteristics of walland flow-related parameters. This provides medical researchers with an efficient method of obtaining a fast overview of patient-specific blood flow data. CCS Concepts • Applied computing → Life and medical sciences; • Human-centered computing → Visualization;
{"title":"Automatic Animations to Analyze Blood Flow Data","authors":"Vikram Apilla, B. Behrendt, K. Lawonn, B. Preim, M. Meuschke","doi":"10.2312/vcbm.20211349","DOIUrl":"https://doi.org/10.2312/vcbm.20211349","url":null,"abstract":"We present an approach for computing camera animations composed of optimal views to support the visual exploration of blood flow data using cerebral aneurysms as major example. Medical researchers are interested in hemodynamic parameters and vessel wall characteristics. The time-dependent character of blood flow data complicates the visual analysis. Our approach is modeled as an optimization problem to automatically determine camera paths during the cardiac cycle. These consist of optimal viewpoints showing regions with suspicious characteristics of walland flow-related parameters. This provides medical researchers with an efficient method of obtaining a fast overview of patient-specific blood flow data. CCS Concepts • Applied computing → Life and medical sciences; • Human-centered computing → Visualization;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"101-105"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73849596","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}
Jana Martschinke, V. Klein, Philipp Kurth, K. Engel, I. Ludolph, T. Hauck, R. Horch, M. Stamminger
Nowadays, many surgical procedures require preoperative planning, mostly relying on data from 3D imaging techniques like computed tomography or magnetic resonance imaging. However, preoperative assessment of this data is carried out on the PC (using classical CT/MR viewing software) and not on the patient’s body itself. Therefore, surgeons need to transfer both their overall understanding of the patient’s individual anatomy and also specific markers and labels for important points from the PC to the patient only with the help of imaginative power or approximative measurement. In order to close the gap between preoperative planning on the PC and surgery on the patient, we propose a system to directly project preoperative knowledge to the body surface by projection mapping. As a result, we are able to display both assigned labels and a volumetric and view-dependent view of the 3D data in-situ. Furthermore, we offer a method to interactively navigate through the data and add 3D markers directly in the projected volumetric view. We demonstrate the benefits of our approach using DIEP flap breast reconstruction as an example. By means of a small pilot study, we show that our method outperforms standard surgical planning in accuracy and can easily be understood and utilized even by persons without any medical knowledge. CCS Concepts • Applied computing → Health informatics; • Computing methodologies → Ray tracing; Mixed / augmented reality;
{"title":"Projection Mapping for In-Situ Surgery Planning by the Example of DIEP Flap Breast Reconstruction","authors":"Jana Martschinke, V. Klein, Philipp Kurth, K. Engel, I. Ludolph, T. Hauck, R. Horch, M. Stamminger","doi":"10.2312/vcbm.20211354","DOIUrl":"https://doi.org/10.2312/vcbm.20211354","url":null,"abstract":"Nowadays, many surgical procedures require preoperative planning, mostly relying on data from 3D imaging techniques like computed tomography or magnetic resonance imaging. However, preoperative assessment of this data is carried out on the PC (using classical CT/MR viewing software) and not on the patient’s body itself. Therefore, surgeons need to transfer both their overall understanding of the patient’s individual anatomy and also specific markers and labels for important points from the PC to the patient only with the help of imaginative power or approximative measurement. In order to close the gap between preoperative planning on the PC and surgery on the patient, we propose a system to directly project preoperative knowledge to the body surface by projection mapping. As a result, we are able to display both assigned labels and a volumetric and view-dependent view of the 3D data in-situ. Furthermore, we offer a method to interactively navigate through the data and add 3D markers directly in the projected volumetric view. We demonstrate the benefits of our approach using DIEP flap breast reconstruction as an example. By means of a small pilot study, we show that our method outperforms standard surgical planning in accuracy and can easily be understood and utilized even by persons without any medical knowledge. CCS Concepts • Applied computing → Health informatics; • Computing methodologies → Ray tracing; Mixed / augmented reality;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"66 1","pages":"145-153"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89333402","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}
Electronic Health Records (EHRs) contain rich medical information about patients, possibly hundreds of notes, lab results, images and other information. Doctors can easily be overwhelmed by this wealth of information. For their daily work, they need to derive narratives from all this information to get insights into the main issues of their patients. Standard solutions show all the information in linear lists, often leading to cognitive overload; research solutions provide timelines and relations between the notes but provide too much fragmented information. We propose MEDeNAR, a system for enabling medical professionals to obtain insights from EHRs based on the different tasks in their workflow. The key aspects of our system are the introduction of an intermediate level that summarizes the information using clustering and NLP methods. The results are visualized along a timeline and provide easy access to the detailed descriptions in notes and lab results at the EHR level. We designed the system using an iterative design process in collaboration with 18 doctors, two nurses and 14 domain experts. During the final evaluation, the doctors ranked our system higher than a standard baseline solution and a variation for the used NLP methods. CCS Concepts • Human-centered computing → Visualization toolkits; User interface toolkits;
{"title":"Multiple Scale Visualization of Electronic Health Records to Support Finding Medical Narratives","authors":"Sanne van der Linden, J. V. Wijk, M. Funk","doi":"10.2312/vcbm.20211351","DOIUrl":"https://doi.org/10.2312/vcbm.20211351","url":null,"abstract":"Electronic Health Records (EHRs) contain rich medical information about patients, possibly hundreds of notes, lab results, images and other information. Doctors can easily be overwhelmed by this wealth of information. For their daily work, they need to derive narratives from all this information to get insights into the main issues of their patients. Standard solutions show all the information in linear lists, often leading to cognitive overload; research solutions provide timelines and relations between the notes but provide too much fragmented information. We propose MEDeNAR, a system for enabling medical professionals to obtain insights from EHRs based on the different tasks in their workflow. The key aspects of our system are the introduction of an intermediate level that summarizes the information using clustering and NLP methods. The results are visualized along a timeline and provide easy access to the detailed descriptions in notes and lab results at the EHR level. We designed the system using an iterative design process in collaboration with 18 doctors, two nurses and 14 domain experts. During the final evaluation, the doctors ranked our system higher than a standard baseline solution and a variation for the used NLP methods. CCS Concepts • Human-centered computing → Visualization toolkits; User interface toolkits;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"34 1","pages":"113-122"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80760744","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}
Vetle Hushagen, Gustav C. Tresselt, N. Smit, K. Specht
Augmented and virtual reality (XR) are potentially powerful tools for enhancing the efficiency of interactive visualization of complex data in biology and medicine. The benefits of visualization of digital objects in XR mainly arise from enhanced depth perception due to the stereoscopic nature of XR head mounted devices. With the added depth dimension, XR is in a prime position to convey complex information and support tasks where 3D information is important. In order to inform the development of novel XR applications in the biology and medicine domain, we present a survey which reviews the neuroscientific basis underlying the immersive features of XR. To make this literature more accessible to the visualization community, we first describe the basics of the visual system, highlighting how visual features are combined to objects and processed in higher cortical areas with a special focus on depth vision. Based on state of the art findings in neuroscience literature related to depth perception, we provide several recommendations for developers and designers. Our aim is to aid development of XR applications and strengthen development of tools aimed at molecular visualization, medical education, and surgery, as well as inspire new application areas. CCS Concepts • General and reference → Surveys and overviews; • Applied computing → Life and medical sciences; • Human-centered computing → Virtual reality; Mixed / augmented reality;
{"title":"The Role of Depth Perception in XR from a Neuroscience Perspective: A Primer and Survey","authors":"Vetle Hushagen, Gustav C. Tresselt, N. Smit, K. Specht","doi":"10.2312/vcbm.20211344","DOIUrl":"https://doi.org/10.2312/vcbm.20211344","url":null,"abstract":"Augmented and virtual reality (XR) are potentially powerful tools for enhancing the efficiency of interactive visualization of complex data in biology and medicine. The benefits of visualization of digital objects in XR mainly arise from enhanced depth perception due to the stereoscopic nature of XR head mounted devices. With the added depth dimension, XR is in a prime position to convey complex information and support tasks where 3D information is important. In order to inform the development of novel XR applications in the biology and medicine domain, we present a survey which reviews the neuroscientific basis underlying the immersive features of XR. To make this literature more accessible to the visualization community, we first describe the basics of the visual system, highlighting how visual features are combined to objects and processed in higher cortical areas with a special focus on depth vision. Based on state of the art findings in neuroscience literature related to depth perception, we provide several recommendations for developers and designers. Our aim is to aid development of XR applications and strengthen development of tools aimed at molecular visualization, medical education, and surgery, as well as inspire new application areas. CCS Concepts • General and reference → Surveys and overviews; • Applied computing → Life and medical sciences; • Human-centered computing → Virtual reality; Mixed / augmented reality;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"52 1","pages":"37-54"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79454514","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}
L. Garrison, M. Meuschke, J. Fairman, N. Smit, B. Preim, S. Bruckner
The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design. CCS Concepts • Human-centered computing → Visualization design and evaluation methods; Scientific visualization; Visualization theory, concepts and paradigms; • Computer Applications → Life and Medical Sciences;
{"title":"An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes","authors":"L. Garrison, M. Meuschke, J. Fairman, N. Smit, B. Preim, S. Bruckner","doi":"10.2312/vcbm.20211339","DOIUrl":"https://doi.org/10.2312/vcbm.20211339","url":null,"abstract":"The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design. CCS Concepts • Human-centered computing → Visualization design and evaluation methods; Scientific visualization; Visualization theory, concepts and paradigms; • Computer Applications → Life and Medical Sciences;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"95 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85713350","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：