Visual Analytics Methods for Modeling in Medical Imaging

医学影像在临床实践中扮演着重要的角色，例如在治疗计划或计算机辅助诊断中。在这方面，医学图像的分割是必要的前提。常用的分割算法基于统计形状模型(SSM)。通过对器官的形状可变性进行建模，它们能够分割仅使用图像强度无法分割的器官。为了建立SSM，必须选择与高维训练数据很好地匹配的模型。由于缺乏关于数据的先验信息，标准模型经常被选择。然而，它们不一定以最佳方式描述数据。在对分割算法进行评估之前，不好的模型选择并不明显。视觉分析方法可以为支持这一建模过程提供有价值的工具。本项目的目的是开发新的视觉分析方法来适应医学图像分割中的SSMS。我们的方法在流程的所有阶段结合了交互式数据可视化、数据分析和模型指导。我们遵循带有反馈环的“闭环系统”概念，允许以交互方式改进模型。通过这种方式，用户可以更深入地了解数据和模型结果之间的对应关系。作为结果，将创建更好的医学图像中器官分割的模型。

Medical imaging plays an important role in clinical practice, for example in treatment planning or computer-aided diagnosis. In this respect, segmentation of medical images is a necessary prerequisite. Frequently used segmentation algorithms are based on statistical shape models (SSMs). By modeling an organ’s shape variability, they enable segmentation of organs which can not be segmented using image intensities only. For building an SSM, models have to be selected that fit the high-dimensional training data well. Due to the lack of prior information on the data, standard models are frequently chosen. However, they do not necessarily describe the data in an optimal way. A poor choice of the model is not apparent until the segmentation algorithm is evaluated. Visual analytics methods can provide valuable tools for supporting this modeling process.The aim of this project is to develop new Visual Analytics methods for fitting SSMs in medical image segmentation. Our approach combines interactive data visualization, data analysis and model steering in all stages of the process. We follow a “closed-loop” concept with feedback loops allowing for refining models interactively. In this way, the user is provided with a deeper insight into the correspondence between data and model result. As an outcome, better models for segmentation of organs in medical images will be created.