Eurographics Workshop on Visual Computing for Biomedicine最新文献

Understanding the Impact of Statistical and Machine Learning Choices on Predictive Models for Radiotherapy 了解统计和机器学习选择对放射治疗预测模型的影响

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221188

Ádám Böröndy, K. Furmanová, R. Raidou

{"title":"Understanding the Impact of Statistical and Machine Learning Choices on Predictive Models for Radiotherapy","authors":"Ádám Böröndy, K. Furmanová, R. Raidou","doi":"10.2312/vcbm.20221188","DOIUrl":"https://doi.org/10.2312/vcbm.20221188","url":null,"abstract":"During radiotherapy (RT) planning, an accurate description of the location and shape of the pelvic organs is a critical factor for the successful treatment of the patient. Yet, during treatment, the pelvis anatomy may differ significantly from the planning phase. A series of recent publications, such as PREVIS [FMCM ∗ 21], have examined alternative approaches to analyzing and predicting pelvic organ variability of individual patients. These approaches are based on a combination of several statistical and machine learning methods, which have not been thoroughly and quantitatively evaluated within the scope of pelvic anatomical variability. Several of their design decisions could have an impact on the outcome of the predictive model. The goal of this work is to assess the impact of alternative choices, focusing mainly on the two key-aspects of shape description and clustering, to generate better predictions for new patients. The results of our assessment indicate that resolution-based descriptors provide more accurate and reliable organ representations than state-of-the-art approaches, while different clustering settings (distance metric and linkage) yield only slightly different clusters. Different clustering methods are able to provide comparable results, although when more shape variability is considered their results start to deviate. These results are valuable for understanding the impact of statistical and machine learning choices on the outcomes of predictive models for anatomical variability.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"28 1","pages":"65-69"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84518325","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: 0

Learning Anatomy through Shared Virtual Reality 通过共享虚拟现实学习解剖学

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221184

José Juan Reyes-Cabrera, José Miguel Santana Núñez, Agustín Trujillo-Pino, M. Maynar, Miguel Ángel Rodríguez Florido

引用次数: 1

HistoContours: a Framework for Visual Annotation of Histopathology Whole Slide Images HistoContours:组织病理学整张幻灯片图像的视觉注释框架

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221192

Khaled A. Althelaya, Faaiz Joad, Nauman Ullah Gilal, W. Mifsud, G. Pintore, E. Gobbetti, Marco Agus, J. Schneider

引用次数: 1

Is there a Tornado in Alex's Blood Flow? A Case Study for Narrative Medical Visualization 亚历克斯的血液里有龙卷风吗?叙事医学可视化的案例研究

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221183

Anna Kleinau, Evgenia Stupak, Eric Mörth, L. Garrison, S. Mittenentzwei, N. Smit, K. Lawonn, S. Bruckner, M. Gutberlet, B. Preim, M. Meuschke

{"title":"Is there a Tornado in Alex's Blood Flow? A Case Study for Narrative Medical Visualization","authors":"Anna Kleinau, Evgenia Stupak, Eric Mörth, L. Garrison, S. Mittenentzwei, N. Smit, K. Lawonn, S. Bruckner, M. Gutberlet, B. Preim, M. Meuschke","doi":"10.2312/vcbm.20221183","DOIUrl":"https://doi.org/10.2312/vcbm.20221183","url":null,"abstract":"Narrative visualization advantageously combines storytelling with new media formats and techniques, like interactivity, to create improved learning experiences. In medicine, it has the potential to improve patient understanding of diagnostic procedures and treatment options, promote confidence, reduce anxiety, and support informed decision-making. However, limited scientific research has been conducted regarding the use of narrative visualization in medicine. To explore the value of narrative visualization in this domain, we introduce a data-driven story to inform a broad audience about the usage of measured blood flow data to diagnose and treat cardiovascular diseases. The focus of the story is on blood flow vortices in the aorta, with which imaging technique they are examined, and why they can be dangerous. In an interdisciplinary team, we define the main contents of the story and the resulting design questions. We sketch the iterative design process and implement the story based on two genres. In a between-subject study, we evaluate the suitability and understandability of the story and the influence of different navigation concepts on user experience. Finally, we discuss reusable concepts for further narrative medical visualization projects.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"1 1","pages":"11-21"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79668134","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: 6

A Stratification Matrix Viewer for Analysis of Neural Network Data 用于神经网络数据分析的分层矩阵查看器

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221194

Philipp Harth, Sumit K. Vohra, D. Udvary, M. Oberländer, H. Hege, D. Baum

引用次数: 2

Perceptual Evaluation of Common Line Variables for Displaying Uncertainty on Molecular Surfaces 用于显示分子表面不确定性的共线变量的感知评价

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221186

A. Sterzik, N. Lichtenberg, M. Krone, D. Cunningham, K. Lawonn

{"title":"Perceptual Evaluation of Common Line Variables for Displaying Uncertainty on Molecular Surfaces","authors":"A. Sterzik, N. Lichtenberg, M. Krone, D. Cunningham, K. Lawonn","doi":"10.2312/vcbm.20221186","DOIUrl":"https://doi.org/10.2312/vcbm.20221186","url":null,"abstract":"Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Speciﬁcally, the effective communication of the uncertainty can inﬂuence the interpretation of the data and the users’ trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Therefore, we conducted a perceptual study to determine the suitability of the line variables sketchiness, dashing, grayscale, and width for distinguishing several uncertainty values on molecular surfaces.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"3 1","pages":"41-51"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91279876","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: 4

Distance Visualizations for Vascular Structures in Desktop and VR: Overview and Implementation 桌面和VR中血管结构的远程可视化:概述和实现

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221182

J. Hombeck, M. Meuschke, S. Lieb, N. Lichtenberg, R. Datta, M. Krone, Christian Hansen, B. Preim, K. Lawonn

引用次数: 3

Studying the Effect of Tissue Properties on Radiofrequency Ablation by Visual Simulation Ensemble Analysis 组织特性对射频消融影响的可视化模拟集成分析

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221187

K. Heimes, Marina Evers, Tim Gerrits, Sandeep Gyawali, D. Sinden, T. Preußer, L. Linsen

引用次数: 0

Understanding Graph Convolutional Networks to detect Brain Lesions from Stroke 理解图卷积网络检测脑卒中的脑损伤

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221195

Ariel Iporre-Rivas, G. Scheuermann, C. Gillmann

引用次数: 0

MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks MuSIC:基于分割掩码的癌症影像数据多序列交互协同配准

Eurographics Workshop on Visual Computing for Biomedicine Pub Date : 2022-01-01 DOI: 10.2312/vcbm.20221190

Tanja Eichner, Eric Mörth, Kari S. Wagner-Larsen, N. Lura, I. Haldorsen, E. Gröller, S. Bruckner, N. Smit

{"title":"MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks","authors":"Tanja Eichner, Eric Mörth, Kari S. Wagner-Larsen, N. Lura, I. Haldorsen, E. Gröller, S. Bruckner, N. Smit","doi":"10.2312/vcbm.20221190","DOIUrl":"https://doi.org/10.2312/vcbm.20221190","url":null,"abstract":"In gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-deﬁned segmentation mask generated for one of the sequences. Our contributions lie in our proposed workﬂow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future.","PeriodicalId":88872,"journal":{"name":"Eurographics Workshop on Visual Computing for Biomedicine","volume":"26 1","pages":"81-91"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82410584","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: 0