Visual Analytics Methods for Modeling in Medical Imaging

医学成像建模的可视化分析方法

• 批准号: 202945761
• 负责人: Professorin Dr.-Ing. Tatiana Landesberger von Antburg
• 金额: --
• 依托单位: Abteilung Informatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/202945761?language=en
• 关键词: Visual; Analytics; Methods; Modeling; Medical

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.

医学成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先决条件。常用的分割算法是基于统计形状模型（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。为了构建SSM，必须选择能够很好地拟合高维训练数据的模型。由于缺乏数据的先验信息，通常选择标准模型。然而，它们不一定以最佳方式描述数据。在对分割算法进行评估之前，模型的不良选择并不明显。可视化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可视化分析方法，用于在医学图像分割中拟合SSM。我们的方法结合了交互式数据可视化，数据分析和模型转向的所有阶段的过程。我们遵循一个“闭环”的概念，允许交互式地改进模型的反馈回路。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。作为一个结果，更好的模型分割的器官在医学图像将被创建。

项目成果

Visual Analytics for model-based medical image segmentation: Opportunities and challenges

• DOI: 10.1016/j.eswa.2013.03.006
• 发表时间: 2013-09
• 期刊: Expert Syst. Appl.
• 作者: T. V. Landesberger;S. Bremm;M. Kirschner;S. Wesarg;Arjan Kuijper

Opening up the “black box” of medical image segmentation with statistical shape models

使用统计形状模型打开医学图像分割的“黑匣子”

• DOI: 10.1007/s00371-013-0852-y
• 发表时间: 2013
• 期刊: The Visual Computer
• 作者: von Landesberger T;Andrienko G;Andrienko N;Bremm S;Kirschner M;Wesarg S;Kujper A.
• 通讯作者: Kujper A.