摘要翻译：
对于医学体积可视化，最重要的任务之一是从三维扫描(CT，MRI…)中揭示临床相关的细节，如冠状动脉，而不是用不重要的部分模糊它们。这些体数据集包含不同的材料，很难用基于衰减系数的一维传递函数进行提取和可视化。多维传递函数允许对数据进行更精确的分类，从而更容易将不同的表面彼此分开。不幸的是，建立多维传递函数可能成为一个相当复杂的任务，通常通过试错来完成。本文首先对神经网络进行了解释，然后提出了一种通过半自动生成传递函数来加快可视化过程的有效方法。我们描述了如何利用神经网络来检测体数据的二维直方图中显示的显著特征，以及如何利用这些信息进行数据分类。
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英文标题：
《Neural networks in 3D medical scan visualization》
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作者：
D\v{z}enan Zuki\'c, Andreas Elsner, Zikrija Avdagi\'c, Gitta Domik
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Graphics        图形学
分类描述：Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
涵盖了计算机图形学的各个方面。大致包括所有ACM课程I.3的材料，除了I.3.5可能有计算几何作为主要的学科领域。
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英文摘要：
  For medical volume visualization, one of the most important tasks is to reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the coronary arteries, without obscuring them with less significant parts. These volume datasets contain different materials which are difficult to extract and visualize with 1D transfer functions based solely on the attenuation coefficient. Multi-dimensional transfer functions allow a much more precise classification of data which makes it easier to separate different surfaces from each other. Unfortunately, setting up multi-dimensional transfer functions can become a fairly complex task, generally accomplished by trial and error. This paper explains neural networks, and then presents an efficient way to speed up visualization process by semi-automatic transfer function generation. We describe how to use neural networks to detect distinctive features shown in the 2D histogram of the volume data and how to use this information for data classification.
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PDF链接：
https://arxiv.org/pdf/0806.2925