Fouché Gwendal, Argelaguet Ferran, Faure Emmanuel, Kervrann Charles
Inria de l'Université de Rennes, IRISA, CNRS, Rennes, France.
LIRMM, Université Montpellier, CNRS, Montpellier, France.
Front Bioinform. 2023 Mar 8;3:998991. doi: 10.3389/fbinf.2023.998991. eCollection 2023.
The analysis of multidimensional time-varying datasets faces challenges, notably regarding the representation of the data and the visualization of temporal variations. We propose an extension of the well-known Space-Time Cube (STC) visualization technique in order to visualize time-varying 3D spatial data, taking advantage of the interaction capabilities of Virtual Reality (VR). First, we propose the Space-Time Hypercube (STH) as an abstraction for 3D temporal data, extended from the STC concept. Second, through the example of embryo development imaging dataset, we detail the construction and visualization of a STC based on a user-driven projection of the spatial and temporal information. This projection yields a 3D STC visualization, which can also encode additional numerical and categorical data. Additionally, we propose a set of tools allowing the user to filter and manipulate the 3D STC which benefits the visualization, exploration and interaction possibilities offered by VR. Finally, we evaluated the proposed visualization method in the context of 3D temporal cell imaging data analysis, through a user study (n = 5) reporting the feedback from five biologists. These domain experts also accompanied the application design as consultants, providing insights on how the STC visualization could be used for the exploration of complex 3D temporal morphogenesis data.
多维时变数据集的分析面临挑战,特别是在数据表示和时间变化可视化方面。我们提出了一种对著名的时空立方体(STC)可视化技术的扩展,以便利用虚拟现实(VR)的交互功能来可视化时变3D空间数据。首先,我们提出时空超立方体(STH)作为3D时间数据的一种抽象,它是从STC概念扩展而来的。其次,通过胚胎发育成像数据集的例子,我们详细介绍了基于空间和时间信息的用户驱动投影构建和可视化STC的过程。这种投影产生了一个3D STC可视化,它还可以编码额外的数值和分类数据。此外,我们提出了一组工具,允许用户对3D STC进行过滤和操作,这有利于VR提供的可视化、探索和交互可能性。最后,我们通过一项用户研究(n = 5),在3D时间细胞成像数据分析的背景下评估了所提出的可视化方法,该研究报告了五位生物学家的反馈。这些领域专家还作为顾问参与了应用程序设计,提供了关于如何将STC可视化用于探索复杂的3D时间形态发生数据的见解。