Karsten Schatz, M. Krone, Tabea L Bauer, V. Ferrario, J. Pleiss, T. Ertl
{"title":"Molecular Sombreros: Abstract Visualization of Binding Sites within Proteins","authors":"Karsten Schatz, M. Krone, Tabea L Bauer, V. Ferrario, J. Pleiss, T. Ertl","doi":"10.2312/VCBM.20191248","DOIUrl":"https://doi.org/10.2312/VCBM.20191248","url":null,"abstract":"","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"51 1","pages":"225-237"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79206971","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. Aubreville, Maximilian Krappmann, C. Bertram, R. Klopfleisch, A. Maier
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45%. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.
{"title":"A Guided Spatial Transformer Network for Histology Cell Differentiation","authors":"M. Aubreville, Maximilian Krappmann, C. Bertram, R. Klopfleisch, A. Maier","doi":"10.2312/vcbm.20171233","DOIUrl":"https://doi.org/10.2312/vcbm.20171233","url":null,"abstract":"Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. \u0000We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45%. \u0000In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"21-25"},"PeriodicalIF":0.0,"publicationDate":"2017-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43475373","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}
Viktor Vad, J. Byška, Adam Jurcík, I. Viola, E. Gröller, H. Hauser, S. Marques, J. Damborský, B. Kozlíková
The function of proteins is tightly related to their interactions with other molecules. The study of such interactions often requires to track the molecules that enter or exit specific regions of the proteins. This is investigated with molecular dynamics simulations, producing the trajectories of thousands of water molecules during hundreds of thousands of time steps. To ease the exploration of such rich spatio-temporal data, we propose a novel workflow for the analysis and visualization of large sets of water-molecule trajectories. Our solution consists of a set of visualization techniques, which help biochemists to classify, cluster, and filter the trajectories and to explore the properties and behavior of selected subsets in detail. Initially, we use an interactive histogram and a time-line visualization to give an overview of all water trajectories and select the interesting ones for further investigation. Further, we depict clusters of trajectories in a novel 2D representation illustrating the flows of water molecules. These views are interactively linked with a 3D representation where we show individual paths, including their simplification, as well as extracted statistical information displayed by isosurfaces. The proposed solution has been designed in tight collaboration with experts to support specific tasks in their scientific workflows. They also conducted several case studies to evaluate the usability and effectiveness of our new solution with respect to their research scenarios. These confirmed that our proposed solution helps in analyzing water trajectories and in extracting the essential information out of the large amount of input data.
{"title":"Watergate: Visual Exploration of Water Trajectories in Protein Dynamics","authors":"Viktor Vad, J. Byška, Adam Jurcík, I. Viola, E. Gröller, H. Hauser, S. Marques, J. Damborský, B. Kozlíková","doi":"10.2312/vcbm.20171235","DOIUrl":"https://doi.org/10.2312/vcbm.20171235","url":null,"abstract":"The function of proteins is tightly related to their interactions with other molecules. The study of such interactions often requires to track the molecules that enter or exit specific regions of the proteins. This is investigated with molecular dynamics simulations, producing the trajectories of thousands of water molecules during hundreds of thousands of time steps. To ease the exploration of such rich spatio-temporal data, we propose a novel workflow for the analysis and visualization of large sets of water-molecule trajectories. Our solution consists of a set of visualization techniques, which help biochemists to classify, cluster, and filter the trajectories and to explore the properties and behavior of selected subsets in detail. Initially, we use an interactive histogram and a time-line visualization to give an overview of all water trajectories and select the interesting ones for further investigation. Further, we depict clusters of trajectories in a novel 2D representation illustrating the flows of water molecules. These views are interactively linked with a 3D representation where we show individual paths, including their simplification, as well as extracted statistical information displayed by isosurfaces. The proposed solution has been designed in tight collaboration with experts to support specific tasks in their scientific workflows. They also conducted several case studies to evaluate the usability and effectiveness of our new solution with respect to their research scenarios. These confirmed that our proposed solution helps in analyzing water trajectories and in extracting the essential information out of the large amount of input data.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"33-42"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42655952","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}
Duc Duy Pham, Cosmin Adrian Morariu, Tobias Terheiden, S. Landgräber, Marcus Jäger, J. Pauli
In this paper, we propose a novel hybrid level set approach that locally balances the combined use of both Gradient Vector Flow and region based energy cost function by means of the Bhattacharyya coefficient. The local neighborhood of each contour point is naturally divided into an area encapsulated and one excluded by the contour. We propose utilizing the Bhattacharyya coefficient of the intensity distributions of these local areas to determine a point-wise weighting scheme for the curve propagation. The performance of our method regarding segmentation quality is evaluated on the segmentation of the hip joint in 10 MRI data sets. Our proposed method shows a clear improvement compared to conventional 3D level set approaches. CCS Concepts •Computing methodologies → Image segmentation; Image processing; •Applied computing → Imaging;
{"title":"MRI Hip Joint Segmentation: A Locally Bhattacharyya Weighted Hybrid 3D Level Set Approach","authors":"Duc Duy Pham, Cosmin Adrian Morariu, Tobias Terheiden, S. Landgräber, Marcus Jäger, J. Pauli","doi":"10.2312/vcbm.20171243","DOIUrl":"https://doi.org/10.2312/vcbm.20171243","url":null,"abstract":"In this paper, we propose a novel hybrid level set approach that locally balances the combined use of both Gradient Vector Flow and region based energy cost function by means of the Bhattacharyya coefficient. The local neighborhood of each contour point is naturally divided into an area encapsulated and one excluded by the contour. We propose utilizing the Bhattacharyya coefficient of the intensity distributions of these local areas to determine a point-wise weighting scheme for the curve propagation. The performance of our method regarding segmentation quality is evaluated on the segmentation of the hip joint in 10 MRI data sets. Our proposed method shows a clear improvement compared to conventional 3D level set approaches. CCS Concepts •Computing methodologies → Image segmentation; Image processing; •Applied computing → Imaging;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"37 1","pages":"113-117"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78329932","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}
Yoni Choukroun, R. Bakalo, Rami Ben-Ari, A. Akselrod-Ballin, Ella Barkan, P. Kisilev
Mammography is the common modality used for screening and early detection of breast cancer. The emergence of machine learning, particularly deep learning methods, aims to assist radiologists to reach higher sensitivity and specificity. Yet, typical supervised machine learning methods demand the radiological images to have findings annotated within the image. This is a tedious task, which is often out of reach due to the high cost and unavailability of expert radiologists. We describe a computeraided detection and diagnosis system for weakly supervised learning, where the mammogram (MG) images are tagged only on a global level, without local annotations. Our work addresses the problem of MG classification and detection of abnormal findings through a novel deep learning framework built on the multiple instance learning (MIL) paradigm. Our proposed method processes the MG image utilizing the full resolution, with a deep MIL convolutional neural network. This approach allows us to classify the whole MG according to a severity score and localize the source of abnormality in full resolution, while trained on a weakly labeled data set. The key hallmark of our approach is automatic discovery of the discriminating patches in the mammograms using MIL. We validate the proposed method on two mammogram data sets, a large multi-center MG cohort and the publicly available INbreast, in two different scenarios. We present promising results in classification and detection, comparable to a recent supervised method that was trained on fully annotated data set. As the volume and complexity of data in healthcare continues to increase, such an approach may have a profound impact on patient care in many applications.
{"title":"Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network","authors":"Yoni Choukroun, R. Bakalo, Rami Ben-Ari, A. Akselrod-Ballin, Ella Barkan, P. Kisilev","doi":"10.2312/vcbm.20171232","DOIUrl":"https://doi.org/10.2312/vcbm.20171232","url":null,"abstract":"Mammography is the common modality used for screening and early detection of breast cancer. The emergence of machine learning, particularly deep learning methods, aims to assist radiologists to reach higher sensitivity and specificity. Yet, typical supervised machine learning methods demand the radiological images to have findings annotated within the image. This is a tedious task, which is often out of reach due to the high cost and unavailability of expert radiologists. We describe a computeraided detection and diagnosis system for weakly supervised learning, where the mammogram (MG) images are tagged only on a global level, without local annotations. Our work addresses the problem of MG classification and detection of abnormal findings through a novel deep learning framework built on the multiple instance learning (MIL) paradigm. Our proposed method processes the MG image utilizing the full resolution, with a deep MIL convolutional neural network. This approach allows us to classify the whole MG according to a severity score and localize the source of abnormality in full resolution, while trained on a weakly labeled data set. The key hallmark of our approach is automatic discovery of the discriminating patches in the mammograms using MIL. We validate the proposed method on two mammogram data sets, a large multi-center MG cohort and the publicly available INbreast, in two different scenarios. We present promising results in classification and detection, comparable to a recent supervised method that was trained on fully annotated data set. As the volume and complexity of data in healthcare continues to increase, such an approach may have a profound impact on patient care in many applications.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"16 1","pages":"11-19"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73941020","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}
Breast cancer is the second most common cause of death in women. Computer-aided diagnosis typically demand for carefully annotated data, precise tumor allocation and delineation of the boundaries, which is rarely available in the medical system. In this paper we present a new deep learning approach for classification of mammograms that requires only a global binary label. Traditional deep learning methods typically employ classification error losses, which are highly biased by class imbalance – a situation that naturally arises in medical classification problems. We hereby suggest a novel loss measure that directly maximizes the Area Under the ROC Curve (AUC), providing an unbiased loss. We validate the proposed model on two mammogram datasets: IMG, comprising of 796 patients, 80 positive (164 images) and 716 negative (1869 images), and the publicly available dataset INbreast. Our results are encouraging, as the proposed scheme achieves an AUC of 0.76 and 0.65 for IMG and INbreast,
{"title":"Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets","authors":"Jeremias Sulam, Rami Ben-Ari, P. Kisilev","doi":"10.2312/vcbm.20171246","DOIUrl":"https://doi.org/10.2312/vcbm.20171246","url":null,"abstract":"Breast cancer is the second most common cause of death in women. Computer-aided diagnosis typically demand for carefully annotated data, precise tumor allocation and delineation of the boundaries, which is rarely available in the medical system. In this paper we present a new deep learning approach for classification of mammograms that requires only a global binary label. Traditional deep learning methods typically employ classification error losses, which are highly biased by class imbalance – a situation that naturally arises in medical classification problems. We hereby suggest a novel loss measure that directly maximizes the Area Under the ROC Curve (AUC), providing an unbiased loss. We validate the proposed model on two mammogram datasets: IMG, comprising of 796 patients, 80 positive (164 images) and 716 negative (1869 images), and the publicly available dataset INbreast. Our results are encouraging, as the proposed scheme achieves an AUC of 0.76 and 0.65 for IMG and INbreast,","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"23 1","pages":"131-135"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74042209","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}
Daniela Modena, E. V. Dijk, D. Bosnacki, H. Eikelder, M. A. Westenberg
Magnetic Resonance-guided High Intensity Focused Ultrasound (MR-HIFU) is a novel and non-invasive therapeutic method. It can be used to locally increase the temperature in a target position in the human body. HIFU procedures are helpful for the treatment of soft tissue tumors and bone metastases. In vivo research with HIFU systems poses several challenges, therefore, a flexible and fast computer model for HIFU propagation and tissue heating is crucial. We introduce HIFUtk, a visual analytics environment to define, perform, and visualize HIFU simulations. We illustrate the use of HIFUtk by applying HIFU to a rabbit bone model, focusing on two common research questions related to HIFU. The first question concerns the relation between the ablated region shape and the focal point position, and the second one concerns the effect of shear waves on the temperature distribution in bone. These use cases demonstrate that HIFUtk provides a flexible visual analytics environment to investigate the effects of HIFU in various type of materials.
{"title":"HIFUtk: Visual Analytics for High Intensity Focused Ultrasound Simulation","authors":"Daniela Modena, E. V. Dijk, D. Bosnacki, H. Eikelder, M. A. Westenberg","doi":"10.2312/vcbm.20171239","DOIUrl":"https://doi.org/10.2312/vcbm.20171239","url":null,"abstract":"Magnetic Resonance-guided High Intensity Focused Ultrasound (MR-HIFU) is a novel and non-invasive therapeutic method. It can be used to locally increase the temperature in a target position in the human body. HIFU procedures are helpful for the treatment of soft tissue tumors and bone metastases. In vivo research with HIFU systems poses several challenges, therefore, a flexible and fast computer model for HIFU propagation and tissue heating is crucial. We introduce HIFUtk, a visual analytics environment to define, perform, and visualize HIFU simulations. We illustrate the use of HIFUtk by applying HIFU to a rabbit bone model, focusing on two common research questions related to HIFU. The first question concerns the relation between the ablated region shape and the focal point position, and the second one concerns the effect of shear waves on the temperature distribution in bone. These use cases demonstrate that HIFUtk provides a flexible visual analytics environment to investigate the effects of HIFU in various type of materials.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"10 3 1","pages":"73-82"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90528391","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}
Using 3D models of medical data for surgery or treatment planning requires a comprehensive visualization of the data. This is crucial to support the physician in creating a cognitive image of the presented model. Vascular models are complex structures and, thus, the correct spatial interpretation is difficult. We propose view-dependent circle glyphs that enhance depth perception in vascular models. The glyphs are automatically placed on vessel end-points in a balanced manner. For this, we introduce a vessel end-point detection algorithm as a pre-processing step and an extensible, feature-driven glyph filtering strategy. Our glyphs are simple to implement and allow an enhanced and quick judgment of the depth value that they represent. We conduct a qualitative evaluation to compare our approach with two existing approaches, that enhance depth perception with illustrative visualization techniques. The evaluation shows that our glyphs perform better in the general case and decisively outperform the reference techniques when it comes to just noticeable differences.
{"title":"Concentric Circle Glyphs for Enhanced Depth-Judgment in Vascular Models","authors":"N. Lichtenberg, C. Hansen, K. Lawonn","doi":"10.2312/vcbm.20171252","DOIUrl":"https://doi.org/10.2312/vcbm.20171252","url":null,"abstract":"Using 3D models of medical data for surgery or treatment planning requires a comprehensive visualization of the data. This is crucial to support the physician in creating a cognitive image of the presented model. Vascular models are complex structures and, thus, the correct spatial interpretation is difficult. We propose view-dependent circle glyphs that enhance depth perception in vascular models. The glyphs are automatically placed on vessel end-points in a balanced manner. For this, we introduce a vessel end-point detection algorithm as a pre-processing step and an extensible, feature-driven glyph filtering strategy. Our glyphs are simple to implement and allow an enhanced and quick judgment of the depth value that they represent. We conduct a qualitative evaluation to compare our approach with two existing approaches, that enhance depth perception with illustrative visualization techniques. The evaluation shows that our glyphs perform better in the general case and decisively outperform the reference techniques when it comes to just noticeable differences.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"40 1","pages":"179-188"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76171620","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. Nim, B. Sommer, Karsten Klein, A. Flack, K. Safi, M. Nagy, W. Fiedler, M. Wikelski, F. Schreiber
Recent advances in miniaturising sensor tags allow to obtain high-resolution bird trajectories, presenting an opportunity for immersive close-up observation of individual and group behaviour in mid-air. The combination of geographical, environmental, and movement data is well suited for investigation in immersive analytics environments. We explore the benefits and requirements of a wide range of such environments, and illustrate a multi-platform immersive analytics solution, based on a tiled 3D display wall and head-mounted displays (Google Cardboard, HTC Vive and Microsoft Hololens). Tailored to biologists studying bird movement data, the immersive environment provides a novel interactive mode to explore the geolocational time-series data. This paper aims to inform the 3D visualisation research community about design considerations obtained from a real world data set in different 3D immersive environments. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the planet-level scale, thereby capturing the public awareness of environmental issues.
{"title":"Design Considerations for Immersive Analytics of Bird Movements Obtained by Miniaturised GPS Sensors","authors":"H. Nim, B. Sommer, Karsten Klein, A. Flack, K. Safi, M. Nagy, W. Fiedler, M. Wikelski, F. Schreiber","doi":"10.2312/vcbm.20171234","DOIUrl":"https://doi.org/10.2312/vcbm.20171234","url":null,"abstract":"Recent advances in miniaturising sensor tags allow to obtain high-resolution bird trajectories, presenting an opportunity for immersive close-up observation of individual and group behaviour in mid-air. The combination of geographical, environmental, and movement data is well suited for investigation in immersive analytics environments. We explore the benefits and requirements of a wide range of such environments, and illustrate a multi-platform immersive analytics solution, based on a tiled 3D display wall and head-mounted displays (Google Cardboard, HTC Vive and Microsoft Hololens). Tailored to biologists studying bird movement data, the immersive environment provides a novel interactive mode to explore the geolocational time-series data. This paper aims to inform the 3D visualisation research community about design considerations obtained from a real world data set in different 3D immersive environments. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the planet-level scale, thereby capturing the public awareness of environmental issues.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"27-31"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88293496","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. Tautz, M. Hüllebrand, M. Steinmetz, Dirk Voit, J. Frahm, A. Hennemuth
Function of the heart, including interventricular septum motion, is influenced by respiration and contraction of the heart muscle. Recent real-time magnetic resonance imaging (MRI) can acquire multi-cycle cardiac data, which enables the analysis of the variation between heart cycles depending on factors such as physical stress or changes in respiration. There are no normal values for this variation in the literature, and there are no established tools for the analysis and exploration of such multi-cycle data available. We propose an analysis and exploration concept that automatically segments the left and right ventricle, extracts motion parameters and allows to interactively explore the results. We tested the concept using nine real-time MRI data sets, including one subject under increasing stress levels and one subject performing a breathing maneuver. All data sets could be automatically processed and then explored successfully, suggesting that our approach can robustly quantify and explore septum thickness in real-time MRI data. CCS Concepts •Human-centered computing → Visual analytics; •Computing methodologies → Image segmentation;
{"title":"Exploration of Interventricular Septum Motion in Multi-Cycle Cardiac MRI","authors":"L. Tautz, M. Hüllebrand, M. Steinmetz, Dirk Voit, J. Frahm, A. Hennemuth","doi":"10.2312/vcbm.20171251","DOIUrl":"https://doi.org/10.2312/vcbm.20171251","url":null,"abstract":"Function of the heart, including interventricular septum motion, is influenced by respiration and contraction of the heart muscle. Recent real-time magnetic resonance imaging (MRI) can acquire multi-cycle cardiac data, which enables the analysis of the variation between heart cycles depending on factors such as physical stress or changes in respiration. There are no normal values for this variation in the literature, and there are no established tools for the analysis and exploration of such multi-cycle data available. We propose an analysis and exploration concept that automatically segments the left and right ventricle, extracts motion parameters and allows to interactively explore the results. We tested the concept using nine real-time MRI data sets, including one subject under increasing stress levels and one subject performing a breathing maneuver. All data sets could be automatically processed and then explored successfully, suggesting that our approach can robustly quantify and explore septum thickness in real-time MRI data. CCS Concepts •Human-centered computing → Visual analytics; •Computing methodologies → Image segmentation;","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"2 1","pages":"169-178"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79162767","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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