发表机构
School of Biomedical Engineering, Tel Aviv University（特拉维夫大学生物医学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对B型超声图像特性，提出采用超声领域特定骨干网络优化LPIPS指标，构建UltraPIPS库，可提升下游任务性能并平衡重建质量与真实性。
AI 中文摘要
在医学成像领域，常用学习型感知图像块相似度（LPIPS）在特征空间中对图像进行语义比较。尽管基于自然图像预训练的骨干网络被广泛用于LPIPS计算，但B型超声图像具有独特的散斑模式和声学特有的图像统计特征，与自然图像乃至放射学中的其他图像存在本质差异。因此，我们提出超声数据的感知相似度测量需要领域特定模型，这一发现并非适用于其他成像模态。我们在分类、分割和重建等下游任务中，对比了使用自然图像、医学通用型及超声骨干网络的LPIPS指标，表明LPIPS骨干网络的选择是一项重要的设计决策。具体而言，超声骨干网络模型与监督模型的下游性能相关性高于经典模型和自然图像模型，且采用超声骨干网络优化LPIPS损失在重建质量和真实性之间实现了良好平衡。我们的代码可在指定URL获取，推出了UltraPIPS库，即基于本文分析的开源基础模型构建的一组LPIPS指标。
英文摘要
In medical imaging, it is common to use learned perceptual image patch similarity (LPIPS) to compare images semantically in feature space. Although backbones pretrained on natural images are widely used for LPIPS computation, B-mode ultrasound images possess distinct speckle patterns and acoustic-specific image statistics that are fundamentally different from natural images and even from other images in radiology. Consequently, we propose that domain-specific models are needed to measure perceptual similarity in ultrasound data, a finding which is not necessarily the case for other imaging modalities. We compare LPIPS metrics across downstream tasks like classification, segmentation and reconstruction using natural image, medical generalist and ultrasound backbone models and show that selection of LPIPS backbone is a non-trivial design choice. In particular, the ultrasound backbone models were more correlated with downstream performance of supervised models than classical and natural image models, and optimization of the LPIPS loss with an ultrasound backbone achieved a strong balance between reconstruction quality and realism. Our code is available at https://github.com/talg2324/UltraPIPS and introduces the UltraPIPS library, a set of LPIPS metrics based on the open-source foundation models analyzed in this paper.
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