Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
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DOI：
10.1016/j.eswa.2013.03.006
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
T. V. Landesberger;S. Bremm;M. Kirschner;S. Wesarg;Arjan Kuijper
中科院分区：
文献类型：
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作者：
T. V. Landesberger;S. Bremm;M. Kirschner;S. Wesarg;Arjan Kuijper
医学图像的分割是临床应用的前提。许多分割算法使用统计形状模型。由于缺乏提供数据先验信息的工具，经常使用标准模型。然而，它们不一定以最佳方式描述数据。可视化分析工具可以支持基于模型的分割，使用户更深入地了解数据和模型结果之间的对应关系。结合这两种方法，更好的模型分割的器官在医学图像中创建。在这项工作中，我们确定了基于模型的图像分割的主要任务和问题。作为一个概念的证明，我们已经小的视觉交互扩展可以是非常有益的。基于这些结果，我们提出了可视化分析在这一领域的研究挑战。
Segmentation of medical images is a prerequisite in clinical practice. Many segmentation algorithms use statistical shape models. Due to the lack of tools providing prior information on the data, standard models are frequently used. However, they do not necessarily describe the data in an optimal way. Model-based segmentation can be supported by Visual Analytics tools, which give the user a deeper insight into the correspondence between data and model result. Combining both approaches, better models for segmentation of organs in medical images are created. In this work, we identify the main tasks and problems in model-based image segmentation. As a proof of concept, we show that already small visual-interactive extensions can be very beneficial. Based on these results, we present research challenges for Visual Analytics in this area.