C. Chalopin, Elisee Ilunga-Mbuyamba, J. G. C. Aragon, Juan Carlos Camacho Rodriguez, F. Arlt, J. Aviña-Cervantes, J. Meixensberger, D. Lindner
Intraoperative ultrasound (iUS) imaging supports neurosurgeons significantly during brain tumor operations. At the beginning of the intervention the integration of the iUS image data within the navigation system guides the surgeon by optimally planning the position and size of the skull opening. After tumor resection, the visualization of the iUS image data enables to identify possible tumor residuals. However, the iUS image data can be complex to interpret. Existing segmentation and registration functions were assembled into pipeline to enhance brain tumor contours in the 3D iUS image data. A brain tumor model, semi-automatically segmented in the preoperative MR data of patients, is rigidly registered with the 3D iUS image using image gradient information. The contour of the registered tumor model is visualized on the monitor of the navigation system. The rigid registration step was offline evaluated on 15 patients who overcame a brain tumor operation. The registered tumor models were compared with manual segmentations of the brain tumor in the 3D iUS data. Averaged DSI values of 82.3% and 68.4% and averaged contour mean distances of 1.7 mm and 3.3 mm were obtained for brain metastases and glioblastomas respectively. Future works will include the improvement of the functions in the pipeline, the integration of the pipeline into a centralized assistance system including further fonctionalities and connected with the navigation system, and the evaluation of the system during brain tumor operations. CCS Concepts •Computing methodologies → 3D imaging; Image segmentation;
{"title":"Application of Image Processing Functions for Brain Tumor Enhancement in Intraoperative Ultrasound Image Data","authors":"C. Chalopin, Elisee Ilunga-Mbuyamba, J. G. C. Aragon, Juan Carlos Camacho Rodriguez, F. Arlt, J. Aviña-Cervantes, J. Meixensberger, D. Lindner","doi":"10.2312/vcbm.20171242","DOIUrl":"https://doi.org/10.2312/vcbm.20171242","url":null,"abstract":"Intraoperative ultrasound (iUS) imaging supports neurosurgeons significantly during brain tumor operations. At the beginning of the intervention the integration of the iUS image data within the navigation system guides the surgeon by optimally planning the position and size of the skull opening. After tumor resection, the visualization of the iUS image data enables to identify possible tumor residuals. However, the iUS image data can be complex to interpret. Existing segmentation and registration functions were assembled into pipeline to enhance brain tumor contours in the 3D iUS image data. A brain tumor model, semi-automatically segmented in the preoperative MR data of patients, is rigidly registered with the 3D iUS image using image gradient information. The contour of the registered tumor model is visualized on the monitor of the navigation system. The rigid registration step was offline evaluated on 15 patients who overcame a brain tumor operation. The registered tumor models were compared with manual segmentations of the brain tumor in the 3D iUS data. Averaged DSI values of 82.3% and 68.4% and averaged contour mean distances of 1.7 mm and 3.3 mm were obtained for brain metastases and glioblastomas respectively. Future works will include the improvement of the functions in the pipeline, the integration of the pipeline into a centralized assistance system including further fonctionalities and connected with the navigation system, and the evaluation of the system during brain tumor operations. CCS Concepts •Computing methodologies → 3D imaging; Image segmentation;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"9 1","pages":"103-111"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89960529","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}
Patrick Löber, Bernhard Stimpel, Christopher Syben, A. Maier, H. Ditt, P. Schramm, Boy Raczkowski, A. Kemmling
In case of an ischemic stroke, identifying and removing blood clots is crucial for a successful recovery. We present a novel method to automatically detect vascular occlusion in non-enhanced computed tomography (NECT) images. Possible hyperdense thrombus candidates are extracted by thresholding and connected component clustering. A set of different features is computed to describe the objects, and a Random Forest classifier is applied to predict them. Thrombus classification yields 98.7% sensitivity with 6.7 false positives per volume, and 91.1% sensitivity with 2.7 false positives per volume. The classifier assigns a clot probability ≥ 90% for every thrombus with a volume larger than 100 mm3 or with a length above 23 mm, and can be used as a reliable method to detect blood clots. CCS Concepts •Computing methodologies → Classification and regression trees; •Applied computing → Health care information systems;
{"title":"Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke","authors":"Patrick Löber, Bernhard Stimpel, Christopher Syben, A. Maier, H. Ditt, P. Schramm, Boy Raczkowski, A. Kemmling","doi":"10.2312/vcbm.20171245","DOIUrl":"https://doi.org/10.2312/vcbm.20171245","url":null,"abstract":"In case of an ischemic stroke, identifying and removing blood clots is crucial for a successful recovery. We present a novel method to automatically detect vascular occlusion in non-enhanced computed tomography (NECT) images. Possible hyperdense thrombus candidates are extracted by thresholding and connected component clustering. A set of different features is computed to describe the objects, and a Random Forest classifier is applied to predict them. Thrombus classification yields 98.7% sensitivity with 6.7 false positives per volume, and 91.1% sensitivity with 2.7 false positives per volume. The classifier assigns a clot probability ≥ 90% for every thrombus with a volume larger than 100 mm3 or with a length above 23 mm, and can be used as a reliable method to detect blood clots. CCS Concepts •Computing methodologies → Classification and regression trees; •Applied computing → Health care information systems;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"27 1","pages":"125-129"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81059073","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, Uli Niemann, T. Ittermann, H. Völzke, D. Schneider, M. Spiliopoulou, B. Preim
We introduce a visual analytics solution to analyze and treat missing values. Our solution is based on general approaches to handle missing values, but is fine-tuned to the problems in epidemiological cohort study data. The most severe missingness problem in these data is the considerable dropout rate in longitudinal studies that limits the power of statistical analysis and the validity of study findings. Our work is inspired by discussions with epidemiologists and tries to add visual components to their current statistics-based approaches. In this paper we provide a graphical user interface for exploration, imputation and checking the quality of imputations.
{"title":"Visual Analytics of Missing Data in Epidemiological Cohort Studies","authors":"S. Alemzadeh, Uli Niemann, T. Ittermann, H. Völzke, D. Schneider, M. Spiliopoulou, B. Preim","doi":"10.2312/vcbm.20171236","DOIUrl":"https://doi.org/10.2312/vcbm.20171236","url":null,"abstract":"We introduce a visual analytics solution to analyze and treat missing values. Our solution is based on general approaches to handle missing values, but is fine-tuned to the problems in epidemiological cohort study data. The most severe missingness problem in these data is the considerable dropout rate in longitudinal studies that limits the power of statistical analysis and the validity of study findings. Our work is inspired by discussions with epidemiologists and tries to add visual components to their current statistics-based approaches. In this paper we provide a graphical user interface for exploration, imputation and checking the quality of imputations.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"13 1","pages":"43-51"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78477237","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}
Julian Hettig, G. Mistelbauer, C. Rieder, K. Lawonn, C. Hansen
Navigated placement of an ablation applicator in liver surgery would benefit from an effective intraoperative visualization of delicate 3D anatomical structures. In this paper, we propose an approach that facilitates surgery with an interactive as well as an animated map display to support navigated applicator placement in the liver. By reducing the visual complexity of 3D anatomical structures, we provide only the most important information on and around a planned applicator path. By employing different illustrative visualization techniques, the applicator path and its surrounding critical structures, such as blood vessels, are clearly conveyed in an unobstructed way. To retain contextual information around the applicator path and its tip, we desaturate these structures with increasing distance. To alleviate time-consuming and tedious interaction during surgery, our visualization is controlled solely by the position and orientation of a tracked applicator. This enables a direct interaction with the map display without interruption of the intervention. Based on our requirement analysis, we conducted a pilot study with eleven participants and an interactive user study with six domain experts to assess the task completion time, error rate, visual parameters and the usefulness of the animation. The outcome of our pilot study shows that our map display facilitates significantly faster decision making (11.8 s vs. 40.9 s) and significantly fewer false assessments of structures at risk (7.4 % vs. 10.3 %) compared to a currently employed 3D visualization. Furthermore, the animation supports timely perception of the course and depth of upcoming blood vessels, and helps to detect possible areas at risk along the path in advance. Hence, the obtained results demonstrate that our proposed interactive map displays exhibit potential to improve the outcome of navigated liver interventions. CCS Concepts •Human-centered computing→ Scientific visualization; Pointing devices; •Computing methodologies→ Non-photorealistic rendering; •Applied computing → Health informatics;
{"title":"Visual Navigation Support for Liver Applicator Placement using Interactive Map Displays","authors":"Julian Hettig, G. Mistelbauer, C. Rieder, K. Lawonn, C. Hansen","doi":"10.2312/vcbm.20171241","DOIUrl":"https://doi.org/10.2312/vcbm.20171241","url":null,"abstract":"Navigated placement of an ablation applicator in liver surgery would benefit from an effective intraoperative visualization of delicate 3D anatomical structures. In this paper, we propose an approach that facilitates surgery with an interactive as well as an animated map display to support navigated applicator placement in the liver. By reducing the visual complexity of 3D anatomical structures, we provide only the most important information on and around a planned applicator path. By employing different illustrative visualization techniques, the applicator path and its surrounding critical structures, such as blood vessels, are clearly conveyed in an unobstructed way. To retain contextual information around the applicator path and its tip, we desaturate these structures with increasing distance. To alleviate time-consuming and tedious interaction during surgery, our visualization is controlled solely by the position and orientation of a tracked applicator. This enables a direct interaction with the map display without interruption of the intervention. Based on our requirement analysis, we conducted a pilot study with eleven participants and an interactive user study with six domain experts to assess the task completion time, error rate, visual parameters and the usefulness of the animation. The outcome of our pilot study shows that our map display facilitates significantly faster decision making (11.8 s vs. 40.9 s) and significantly fewer false assessments of structures at risk (7.4 % vs. 10.3 %) compared to a currently employed 3D visualization. Furthermore, the animation supports timely perception of the course and depth of upcoming blood vessels, and helps to detect possible areas at risk along the path in advance. Hence, the obtained results demonstrate that our proposed interactive map displays exhibit potential to improve the outcome of navigated liver interventions. CCS Concepts •Human-centered computing→ Scientific visualization; Pointing devices; •Computing methodologies→ Non-photorealistic rendering; •Applied computing → Health informatics;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"34 1","pages":"93-102"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73788498","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}
Image-guided thermal ablation procedures such as microwave ablation (MWA) or radiofrequency ablation (RFA) have become clinically accepted treatment options for liver tumors. The goal of these minimally invasive procedures is the destruction of focal liver malignancies using mostly needle-shaped instruments. Computed tomography (CT) imaging may be used to navigate the applicator to the target position in order to achieve complete tumor ablation. Due to limited image quality and resolution, the treatment target and risk structures may be hardly visible in intra-interventional CT-images, hampering verification of the intended applicator position. In this work, we propose a navigation guidance method based only on CT images to support the physician with additional information to reach the target position. Therefore, planning information extracted from pre-interventional images is fused with the current intra-interventional image. The visible applicator is extracted semi-automatically from the intra-interventional image. The localization of the needle instrument is used to guide the physician by display of the pathway, projection of anatomical structures, and correction suggestions. In an evaluation, we demonstrate the potential of the proposed method to improve the clinical success rate of complex liver tumor ablations while increasing the accuracy and reducing the number of intra-interventional CT images needed. CCS Concepts •Human-centered computing → Scientific visualization; •Computing methodologies → Object detection; •Applied computing → Health informatics;
{"title":"CT-Based Navigation Guidance for Liver Tumor Ablation","authors":"J. Alpers, C. Hansen, K. Ringe, C. Rieder","doi":"10.2312/vcbm.20171240","DOIUrl":"https://doi.org/10.2312/vcbm.20171240","url":null,"abstract":"Image-guided thermal ablation procedures such as microwave ablation (MWA) or radiofrequency ablation (RFA) have become clinically accepted treatment options for liver tumors. The goal of these minimally invasive procedures is the destruction of focal liver malignancies using mostly needle-shaped instruments. Computed tomography (CT) imaging may be used to navigate the applicator to the target position in order to achieve complete tumor ablation. Due to limited image quality and resolution, the treatment target and risk structures may be hardly visible in intra-interventional CT-images, hampering verification of the intended applicator position. In this work, we propose a navigation guidance method based only on CT images to support the physician with additional information to reach the target position. Therefore, planning information extracted from pre-interventional images is fused with the current intra-interventional image. The visible applicator is extracted semi-automatically from the intra-interventional image. The localization of the needle instrument is used to guide the physician by display of the pathway, projection of anatomical structures, and correction suggestions. In an evaluation, we demonstrate the potential of the proposed method to improve the clinical success rate of complex liver tumor ablations while increasing the accuracy and reducing the number of intra-interventional CT images needed. CCS Concepts •Human-centered computing → Scientific visualization; •Computing methodologies → Object detection; •Applied computing → Health informatics;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"37 1","pages":"83-92"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78667118","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}
Daniele Pezzatini, C. Yagüe, P. Rudenick, J. Blat, B. Bijnens, O. Camara
Comunicacio presentada al congres Eurographics Workshop on Visual Computing for Biology and Medicine que va tenir lloc el 7 i 8 de setembre a Bremen, Alemanya.
{"title":"A Web-Based Tool for Cardiac Dyssynchrony Assessment on Ultrasound Data","authors":"Daniele Pezzatini, C. Yagüe, P. Rudenick, J. Blat, B. Bijnens, O. Camara","doi":"10.2312/vcbm.20171247","DOIUrl":"https://doi.org/10.2312/vcbm.20171247","url":null,"abstract":"Comunicacio presentada al congres Eurographics Workshop on Visual Computing for Biology and Medicine que va tenir lloc el 7 i 8 de setembre a Bremen, Alemanya.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"137-141"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72989998","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. Shakya, Xuan Gu, Nazre Batool, E. Özarslan, H. Knutsson
Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model ...
{"title":"Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions","authors":"S. Shakya, Xuan Gu, Nazre Batool, E. Özarslan, H. Knutsson","doi":"10.2312/vcbm.20171244","DOIUrl":"https://doi.org/10.2312/vcbm.20171244","url":null,"abstract":"Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model ...","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"111 1","pages":"119-123"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77879336","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}
Cavities are crucial for interactions of proteins with other molecules. While a variety of different cavity types exists, tunnels in particular play an important role, as they enable a ligand to deeply enter the active site of a protein where chemical reactions can undergo. Consequently, domain scientists are interested in understanding properties relevant for binding interactions inside molecular tunnels. Unfortunately, when inspecting a 3D representation of the molecule under investigation, tunnels are difficult to analyze due to occlusion issues. Therefore, within this paper we propose a novel reprojection technique that transforms the 3D structure of a molecule to obtain a 2D representation of the tunnel interior. The reprojection has been designed with respect to application-oriented design guidelines, we have identified together with our domain partners. To comply with these guidelines, the transformation preserves individual residues, while the result is capable of showing binding properties inside the tunnel without suffering from occlusions. Thus the reprojected tunnel interior can be used to display physico-chemical properties, e.g., hydrophobicity or amino acid orientation, of residues near a tunnel’s surface. As these properties are essential for the interaction between protein and ligand, they can thus hint angles of attack for protein engineers. To demonstrate the benefits of the developed visualization, the obtained results are discussed with respect to domain expert feedback.
{"title":"Protein Tunnel Reprojection for Physico-Chemical Property Analysis","authors":"Jan Malzahn, B. Kozlíková, T. Ropinski","doi":"10.2312/vcbm.20171231","DOIUrl":"https://doi.org/10.2312/vcbm.20171231","url":null,"abstract":"Cavities are crucial for interactions of proteins with other molecules. While a variety of different cavity types exists, tunnels in particular play an important role, as they enable a ligand to deeply enter the active site of a protein where chemical reactions can undergo. Consequently, domain scientists are interested in understanding properties relevant for binding interactions inside molecular tunnels. Unfortunately, when inspecting a 3D representation of the molecule under investigation, tunnels are difficult to analyze due to occlusion issues. Therefore, within this paper we propose a novel reprojection technique that transforms the 3D structure of a molecule to obtain a 2D representation of the tunnel interior. The reprojection has been designed with respect to application-oriented design guidelines, we have identified together with our domain partners. To comply with these guidelines, the transformation preserves individual residues, while the result is capable of showing binding properties inside the tunnel without suffering from occlusions. Thus the reprojected tunnel interior can be used to display physico-chemical properties, e.g., hydrophobicity or amino acid orientation, of residues near a tunnel’s surface. As these properties are essential for the interaction between protein and ligand, they can thus hint angles of attack for protein engineers. To demonstrate the benefits of the developed visualization, the obtained results are discussed with respect to domain expert feedback.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"10 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84358414","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}
The presence of depth cues in a visualization can be a great aid in understanding the structure and topology of a vessel tree. Pseudo Chromadepth is a well-known technique for enhancing depth perception in vascular 3D models. Since it strongly relies on the color channel to convey its depth cues, it is traditionally not suited for combined visualizations comprising color-encoded surface parameters. In this paper, we present and evaluate the use of a modified form of Pseudo Chromadepth that supports displaying additional surface parameters using the color channel while still increasing depth perception. This technique has been designed for the visualization of cerebral aneurysm models. We have combined a discretized color scale to visualize the surface parameter with the Pseudo Chromadepth color scale to convey depth using a Fresnel-inspired blending mask. To evaluate our approach, we have conducted two consecutive studies. The first was performed with 104 participants from the general public and the second with eleven experts in the fields of medical engineering and flow simulation. These studies show that Pseudo Chromadepth can be used in conjunction with color-encoded surface attributes to support depth perception as long as the color scale is chosen appropriately.
{"title":"Combining Pseudo Chroma Depth Enhancement and Parameter Mapping for Vascular Surface Models","authors":"B. Behrendt, P. Berg, B. Preim, S. Saalfeld","doi":"10.2312/vcbm.20171250","DOIUrl":"https://doi.org/10.2312/vcbm.20171250","url":null,"abstract":"The presence of depth cues in a visualization can be a great aid in understanding the structure and topology of a vessel tree. Pseudo Chromadepth is a well-known technique for enhancing depth perception in vascular 3D models. Since it strongly relies on the color channel to convey its depth cues, it is traditionally not suited for combined visualizations comprising color-encoded surface parameters. In this paper, we present and evaluate the use of a modified form of Pseudo Chromadepth that supports displaying additional surface parameters using the color channel while still increasing depth perception. This technique has been designed for the visualization of cerebral aneurysm models. We have combined a discretized color scale to visualize the surface parameter with the Pseudo Chromadepth color scale to convey depth using a Fresnel-inspired blending mask. To evaluate our approach, we have conducted two consecutive studies. The first was performed with 104 participants from the general public and the second with eleven experts in the fields of medical engineering and flow simulation. These studies show that Pseudo Chromadepth can be used in conjunction with color-encoded surface attributes to support depth perception as long as the color scale is chosen appropriately.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"475 1","pages":"159-168"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79936374","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}
Changgong Zhang, T. Höllt, M. Caan, E. Eisemann, A. Vilanova
Diffusion Tensor Imaging (DTI) group studies often require the comparison of two groups of 3D diffusion tensor fields. The total number of datasets involved in the study and the multivariate nature of diffusion tensors together make this a challenging process. The traditional approach is to reduce the six-dimensional diffusion tensor to some scalar quantities, which can be analyzed with univariate statistical methods, and visualized with standard techniques such as slice views. However, this provides merely part of the whole story due to information reduction. If to take the full tensor information into account, only few methods are available, and they focus on the analysis of a single group, rather than the comparison of two groups. Simultaneously comparing two groups of diffusion tensor fields by simple juxtaposition or superposition is rather impractical. In this work, we extend previous work by Zhang et al. [ZCH* 17] to visually compare two groups of diffusion tensor fields. To deal with the wealth of information, the comparison is carried out at multiple levels of detail. In the 3D spatial domain, we propose a details-on-demand glyph representation to support the visual comparison of the tensor ensemble summary information in a progressive manner. The spatial view guides analysts to select voxels of interest. Then at the detail level, the respective original tensor ensembles are compared in terms of tensor intrinsic properties, with special care taken to reduce visual clutter. We demonstrate the usefulness of our visual analysis system by comparing a control group and an HIV positive patient group.
{"title":"Comparative Visualization for Diffusion Tensor Imaging Group Study at Multiple Levels of Detail","authors":"Changgong Zhang, T. Höllt, M. Caan, E. Eisemann, A. Vilanova","doi":"10.2312/vcbm.20171237","DOIUrl":"https://doi.org/10.2312/vcbm.20171237","url":null,"abstract":"Diffusion Tensor Imaging (DTI) group studies often require the comparison of two groups of 3D diffusion tensor fields. The total number of datasets involved in the study and the multivariate nature of diffusion tensors together make this a challenging process. The traditional approach is to reduce the six-dimensional diffusion tensor to some scalar quantities, which can be analyzed with univariate statistical methods, and visualized with standard techniques such as slice views. However, this provides merely part of the whole story due to information reduction. If to take the full tensor information into account, only few methods are available, and they focus on the analysis of a single group, rather than the comparison of two groups. Simultaneously comparing two groups of diffusion tensor fields by simple juxtaposition or superposition is rather impractical. In this work, we extend previous work by Zhang et al. [ZCH* 17] to visually compare two groups of diffusion tensor fields. To deal with the wealth of information, the comparison is carried out at multiple levels of detail. In the 3D spatial domain, we propose a details-on-demand glyph representation to support the visual comparison of the tensor ensemble summary information in a progressive manner. The spatial view guides analysts to select voxels of interest. Then at the detail level, the respective original tensor ensembles are compared in terms of tensor intrinsic properties, with special care taken to reduce visual clutter. We demonstrate the usefulness of our visual analysis system by comparing a control group and an HIV positive patient group.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"38 1","pages":"53-62"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88528995","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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