The progression of X-ray technology introduces diverse image styles that need to be adapted to the preferences of radiologists. To support this task, we introduce a novel deep learning-based metric that quantifies style differences of non-matching image pairs. At the heart of our metric is an encoder capable of generating X-ray image style representations. This encoder is trained without any explicit knowledge of style distances by exploiting Simple Siamese learning. During inference, the style representations produced by the encoder are used to calculate a distance metric for non-matching image pairs. Our experiments investigate the proposed concept for a disclosed reproducible and a proprietary image processing pipeline along two dimensions: First, we use a t-distributed stochastic neighbor embedding (t-SNE) analysis to illustrate that the encoder outputs provide meaningful and discriminative style representations. Second, the proposed metric calculated from the encoder outputs is shown to quantify style distances for non-matching pairs in good alignment with the human perception. These results confirm that our proposed method is a promising technique to quantify style differences, which can be used for guided style selection as well as automatic optimization of image pipeline parameters.

翻译：X射线技术的进步引入了多样化的图像风格，这些风格需要适应放射科医师的偏好。为支持此任务，我们提出了一种基于深度学习的新型度量方法，用于量化非匹配图像对之间的风格差异。该度量的核心是一个能够生成X射线图像风格表示的编码器。该编码器通过利用简单孪生学习进行训练，无需任何显式的风格距离先验知识。在推理阶段，编码器生成的风格表示被用于计算非匹配图像对的距离度量。我们的实验从两个维度研究了所提出方法在公开可重复及专有图像处理流程中的表现：首先，我们使用t分布随机邻域嵌入分析证明编码器输出能提供有意义且具区分度的风格表示；其次，基于编码器输出计算得到的度量被证明能够量化非匹配图像对的风格距离，且与人类感知高度一致。这些结果证实，我们提出的方法是量化风格差异的有效技术，可用于引导式风格选择以及图像处理流程参数的自动优化。