Abstract
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 this https URL.
Abstract (translated)
冻切片（FS）技术是一种快速而有效的解决方案，用于在手术过程中为病理学家评估切片，仅需15-30分钟，允许在短时间内做出进一步的手术干预决策。然而，FS过程通常引入收缩和错乱的伪像，如褶皱和冰晶效应。相比之下，高质量正式冻固定装片（FFPE）的这些伪像和错乱是缺失的，这些装片需要2-3天来准备。虽然基于生成对抗网络（GAN）的方法已经将FS翻译为FFPE图像（F2F），但它们可能留下形态不准确残余FS伪像或引入新的伪像，从而降低这些翻译的质量和临床评估的准确性。在这项研究中，我们对比了最近的几种生成模型，重点关注GAN和潜在扩散模型（LDM），以克服这些限制。我们引入了一种结合LDM和病理学预训练嵌入的新方法，以增强FS图像的修复。我们的框架利用由文本和预训练嵌入条件下的LDM学习有意义FS和FFPE病理学图像的特征。通过扩散和去噪技术，我们的方法不仅保留色彩染色和组织形态等基本诊断特征，还提出了一个嵌入转译机制，以更好地预测输入FS图像的目标FFPE表示。因此，这项工作在分类性能上取得了显著的改进，曲线下的面积从81.99%增加至94.64%，同时具有优势的CaseFD。这项工作为FS到FFPE图像翻译质量设立了新的基准，承诺在病理学FS图像分析中提高可靠性和准确性。我们的工作可以在以下链接处查看：
URL
https://arxiv.org/abs/2404.12650
