Uncertainty in Medical Visualization: Bringing Imaging and Simulation Uncertainty Together

可视化方法已成为临床常规辅助诊断、治疗计划和术中辅助不可或缺的一部分。医学可视化是基于某些假设创建的，通常不会使医学专家意识到这些假设，这些假设可能导致所显示的图片与实际情况存在潜在偏差。因此，医学专家经常将可视化视为真实的图像，并将其解释为决策的基础，至少是部分基础。然而，从实际图像采集、配准和分割任务到最终渲染的医疗可视化流程包含许多潜在的错误来源。为了做出更明智的决策，需要定量地捕捉这些错误概率的影响，并以视觉方式传达给医学专家。这就是不确定性可视化的任务。在第一个资助期内，我们对出现的不确定性进行了严格的建模，并开发了可视化编码方法，从而形成了用于不确定性意识决策的交互式可视化分析系统。除了利用来自医学影像数据的信息外，还可以通过模拟生物物理或医学过程的患者特定信息来增强系统，从而进一步改进决策。另一方面，这种模拟也是基于不确定的成像数据，甚至做出进一步的假设。因此，他们的结果也包含潜在的具有临床相关性的不确定性。我们的目标是将成像不确定性和模拟不确定性结合在一个视觉分析系统中。利用第一个资助期的不确定性感知图像分割，我们建议使用概率输入开发不确定性感知患者特定模拟，使用深度学习方法预测模拟结果，以及交互式可视化，以便对来自分割和模拟的患者特定数据进行不确定性感知分析。这些方法应用于医学应用中，其中可视化起着至关重要的作用。

Visualization methods have become an integral part of clinical routine supporting diagnosis, treatment planning, and intraoperative assistance. The medical visualizations are created based on certain assumptions and typically do not make the medical experts aware of those assumptions which may result in potential deviations of the shown picture from the actual situation. Hence, the medical experts often perceive andinterpret the visualization as a true image, on which decisions are based, at least, in part. However, the medical visualization pipeline ranging from the actual image acquisition over registration and segmentation tasks to the final rendering includes many potential sources of errors. To allow for a more educated decision making, the impact of those error probabilities need to be quantitatively captured and visually conveyed to themedical expert. This is the task of uncertainty visualization. Within the first funding period, we developed a rigorous modeling of the appearing uncertainties and methods for visually encoding them, leading to interactive visual analysis systems for uncertainty-aware decision making. Beside using information derived from medical imaging data, decision making can further be improved by enhancing the system with patient-specific information from simulating biophysical or medical processes. Such simulations, on the other hand, are also based on the uncertain imaging data and even make further assumptions. Thus, their results also contain uncertainty potentially bearing clinical relevance. Our goal is to bring together imaging uncertainty and simulation uncertainty in a visual analysis system. Using the uncertainty-aware image segmentation of the first funding period, we propose to develop uncertainty-aware patient-specific simulations using probabilistic inputs, predictions of the simulation outcomes using deep-learning methods, and interactive visualizations for an uncertainty-aware analysis of patient-specific data from segmentations and simulations. The methods shall be employed within medical applications, where visualization plays a crucial role.