The Frozen Section (FS) technique is a rapid and efficient method, taking only 15-30 minutes to prepare slides for pathologists' evaluation during surgery, enabling immediate decisions on further surgical interventions. However, FS process often introduces artifacts and distortions like folds and ice-crystal effects. In contrast, these artifacts and distortions are absent in the higher-quality formalin-fixed paraffin-embedded (FFPE) slides, which require 2-3 days to prepare. While Generative Adversarial Network (GAN)-based methods have been used to translate FS to FFPE images (F2F), they may leave morphological inaccuracies with remaining FS artifacts or introduce new artifacts, reducing the quality of these translations for clinical assessments. In this study, we benchmark recent generative models, focusing on GANs and Latent Diffusion Models (LDMs), to overcome these limitations. We introduce a novel approach that combines LDMs with Histopathology Pre-Trained Embeddings to enhance restoration of FS images. Our framework leverages LDMs conditioned by both text and pre-trained embeddings to learn meaningful features of FS and FFPE histopathology images. Through diffusion and denoising techniques, our approach not only preserves essential diagnostic attributes like color staining and tissue morphology but also proposes an embedding translation mechanism to better predict the targeted FFPE representation of input FS images. As a result, this work achieves a significant improvement in classification performance, with the Area Under the Curve rising from 81.99% to 94.64%, accompanied by an advantageous CaseFD. This work establishes a new benchmark for FS to FFPE image translation quality, promising enhanced reliability and accuracy in histopathology FS image analysis. Our work is available at https://minhmanho.github.io/f2f_ldm/.

翻译：冰冻切片（FS）技术是一种快速高效的方法，仅需15-30分钟即可制备病理学家术中评估所需的切片，从而能即时决定后续手术方案。然而，FS过程常引入褶皱、冰晶效应等伪影和形变。相比之下，更高质量的石蜡包埋（FFPE）切片需要2-3天制备，却不存在这些伪影与变形。尽管基于生成对抗网络（GAN）的方法已被用于将FS图像转换为FFPE图像（F2F），但这类方法可能残留FS伪影导致形态学不准确，或引入新伪影，降低了临床评估中翻译图像的质量。本研究对最新生成模型进行基准测试，重点关注GAN和潜扩散模型（LDM）以突破这些局限。我们提出一种创新方法，将LDM与组织病理学预训练嵌入相结合，增强FS图像恢复能力。本框架利用文本和预训练嵌入共同条件化的LDM，学习FS与FFPE组织病理学图像的有意义特征。通过扩散与去噪技术，该方法不仅能保留染色颜色、组织形态等关键诊断属性，还提出一种嵌入翻译机制以更好预测输入FS图像对应的目标FFPE表征。最终，本工作在分类性能上取得显著提升，曲线下面积从81.99%提升至94.64%，并伴随有利的CaseFD指标。本研究为FS到FFPE图像翻译质量建立了新基准，有望提升组织病理学FS图像分析的可靠性与准确性。本研究代码及资源见 https://minhmanho.github.io/f2f_ldm/。