Visual Analytics Methods for Modeling in Medical Imaging

医学影像在临床实践中发挥着重要作用，例如在治疗计划或计算机辅助诊断中。在这方面，医学图像的分割是一个必要的前提。常用的分割算法是基于统计形状模型的分割算法。通过模拟器官的形状可变性，他们可以分割器官，不能分割仅使用图像强度。为了构建SSM，必须选择与高维训练数据很好拟合的模型。由于缺乏数据的先验信息，通常选择标准模型。然而，它们不一定以最优的方式描述数据。一个糟糕的模型选择是不明显的，直到分割算法被评估。可视化分析方法可以为支持这个建模过程提供有价值的工具。这个项目的目的是开发新的视觉分析方法来拟合医学图像分割中的ssm。我们的方法结合了交互式数据可视化，数据分析和模型导向的所有阶段的过程。我们遵循一个带有反馈回路的“闭环”概念，允许交互地改进模型。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。因此，将创建更好的医学图像中器官分割模型。

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.