ViES: Visual-interactive Exploration for individualized Selection of relevant data regions

本项目旨在支持青光眼视网膜损伤的交互式视觉诊断。工业应用合作伙伴海德堡工程有限公司（Heidelberg Engineering GmbH）提供了高分辨率的数据集，需要对其进行简化以进行评估。目前，仅应用了高度简化的自动数据缩减，这可能导致误解，特别是在视网膜小变化的情况下。这些自动化计算将通过新颖的视觉交互方法得到扩展。在多个尺度上呈现数据，提取和跟踪特定的特征，以及视觉上的强调和链接，以支持相关细节的选择。通过这种方式，生成了更小的个性化数据集，便于医生管理，并允许对视网膜亚结构进行全面分析。在这个项目的范围内，我们的目标是开发一个框架，它支持这些不同步骤之间的无缝过渡：数据选择，减少和细节的可视化分析。为了设计框架，我们可以利用DFG优先级程序1335（可扩展可视化分析）中先前开发的方法：一种可视化大型多尺度数据集的新技术。相邻尺度之间的异质性值编码为寻找有趣的细节提供了指导。2. 一种可视化数据、不确定性和相关参数依赖关系的新技术。3. 一种新的3-dim特征提取与跟踪技术。数据集。这些方法将根据给定的应用场景-视网膜损伤的诊断进行调整和增强。这需要进行以下基本调查：对1-dim进行了多尺度可视化。时间序列。虽然是表示2-dim的第一个扩展。数据存在，处理3-dim。数据需要大量的增强。2. 设计了数据、不确定性和参数依赖性的可视化方法来表示1-dim。多运行仿真数据。处理高分辨率3-dim。数据集提出了新的问题和挑战。3. 应用场景需要规范领域特定的特征，并开发新的方法来提取和可视化这些特征。为了还原和视觉呈现视网膜数据，必须满足一定的质量标准。例如，我们需要将两者形象化；选择的数据本身，以及应用的选择标准。为此目的，将制定适当的方法。

This project aims at supporting the diagnosis of glaucoma induced retinal damage by interactive visual means. The industrial application partner, Heidelberg Engineering GmbH, provides high-resolution data sets that need to be reduced for their assessment. Currently, only a strongly simplified automated data reduction is applied, which can lead to misinterpretations, particularly in the case of small retinal changes. These automated computations will be extended with novel visual interactive methods. Presenting the data at multiple scales, extracting and tracking specific features, as well as visual accentuation and linking will be applied to support the selection of relevant details. In this way, smaller individualized data sets are generated that are easily manageable by the physicians and allow for a comprehensive analysis of retinal sub structures. Within the scope of this project we aim at developing a framework, which supports seamless transitions between these different steps: data selection, reduction and visual analysis of details. To design the framework, we can take advantage of previously developed approaches in the DFG priority program 1335 (Scalable Visual Analytics): 1. A novel technique to visualize large multi-scale data sets. The encoding of heterogeneity values between adjacent scales provides guidance for finding interesting details. 2. A novel technique to visualize data, uncertainties, and related parameter dependencies. 3. A novel technique to extract and track features in 3-dim. data sets. These approaches will be adapted and enhanced with regard to the given application scenario - the diagnosis of retinal damage. This requires the following fundamental investigations: 1. The multi-scale visualization was developed for 1-dim. time series. Although a first extension for representing 2-dim. data exists, handling 3-dim. data requires substantial enhancements. 2. The visualization approach for data, uncertainties and parameter dependencies was designed for representing 1-dim. multi-run simulation data. Dealing with high-resolution 3-dim. data sets raises new issues and challenges. 3. The application scenario requires the specification of domain specific features and the development of novel methods for their extraction and visualization. For the reduction and visual presentation of the retinal data certain quality criteria have to be met. For example, we need to visualize both; the selected data itself, but also the applied selection criteria. For this purpose, appropriate methods will be developed.