Eurographics Workshop on Visual Computing for Biomedicine最新文献_第8页

Molecular Sombreros: Abstract Visualization of Binding Sites within Proteins 分子宽边帽:蛋白质结合位点的抽象可视化

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2019-01-01 DOI: 10.2312/VCBM.20191248

Karsten Schatz, M. Krone, Tabea L Bauer, V. Ferrario, J. Pleiss, T. Ertl

引用次数: 2

A Guided Spatial Transformer Network for Histology Cell Differentiation 一种用于组织细胞分化的引导空间变换网络

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-07-26 DOI: 10.2312/vcbm.20171233

M. Aubreville, Maximilian Krappmann, C. Bertram, R. Klopfleisch, A. Maier

{"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}

引用次数: 10

Watergate: Visual Exploration of Water Trajectories in Protein Dynamics 水门:蛋白质动力学中水轨迹的视觉探索

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-07-09 DOI: 10.2312/vcbm.20171235

Viktor Vad, J. Byška, Adam Jurcík, I. Viola, E. Gröller, H. Hauser, S. Marques, J. Damborský, B. Kozlíková

{"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}

引用次数: 10

MRI Hip Joint Segmentation: A Locally Bhattacharyya Weighted Hybrid 3D Level Set Approach MRI髋关节分割:局部Bhattacharyya加权混合三维水平集方法

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171243

Duc Duy Pham, Cosmin Adrian Morariu, Tobias Terheiden, S. Landgräber, Marcus Jäger, J. Pauli

引用次数: 3

Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network 基于深度多实例神经网络的乳房x线照片分类与非局部标记异常检测

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171232

Yoni Choukroun, R. Bakalo, Rami Ben-Ari, A. Akselrod-Ballin, Ella Barkan, P. Kisilev

{"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}

引用次数: 26

Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets 利用深度学习最大化不平衡乳房x线照片数据集分类的AUC

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171246

Jeremias Sulam, Rami Ben-Ari, P. Kisilev

引用次数: 18

HIFUtk: Visual Analytics for High Intensity Focused Ultrasound Simulation 高强度聚焦超声模拟的可视化分析

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171239

Daniela Modena, E. V. Dijk, D. Bosnacki, H. Eikelder, M. A. Westenberg

引用次数: 1

Concentric Circle Glyphs for Enhanced Depth-Judgment in Vascular Models 增强血管模型深度判断的同心圆符号

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171252

N. Lichtenberg, C. Hansen, K. Lawonn

引用次数: 14

Design Considerations for Immersive Analytics of Bird Movements Obtained by Miniaturised GPS Sensors 小型化GPS传感器获得的鸟类运动沉浸式分析的设计考虑

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171234

H. Nim, B. Sommer, Karsten Klein, A. Flack, K. Safi, M. Nagy, W. Fiedler, M. Wikelski, F. Schreiber

{"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}

引用次数: 8

Exploration of Interventricular Septum Motion in Multi-Cycle Cardiac MRI 多周期心脏MRI对室间隔运动的探讨

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2017-01-01 DOI: 10.2312/vcbm.20171251

L. Tautz, M. Hüllebrand, M. Steinmetz, Dirk Voit, J. Frahm, A. Hennemuth

{"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}

引用次数: 2