In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method.

翻译：在体渲染中，传递函数用于对感兴趣结构进行分类，并赋予颜色和不透明度等光学属性。它们通常被定义为将简单特征映射到这些光学属性的一维或二维函数。由于设计传递函数的过程通常繁琐且不直观，已有多种方法被提出用于其交互式规定。本文提出了一种新方法，通过利用自监督预训练视觉变换器的特征提取能力来定义体渲染的传递函数。为设计传递函数，用户只需在切片查看器中选择感兴趣结构，我们的方法便会基于神经网络提取的高级特征自动选择相似结构。与以往基于学习的传递函数方法不同，本方法无需模型训练并支持快速推理，从而实现体数据的交互式探索。我们的方法通过交互式告知用户当前分类状态来减少必要的标注量，使用户能够专注于仍需标注的感兴趣结构。在实践中，这使得用户能够在数秒而非数分钟内设计出传递函数。我们在标注时间、计算时间以及分割精度方面，将本方法与现有基于学习的方法进行了比较。随附视频展示了我们方法的交互性与有效性。