将术中冰冻切片转为高质量石蜡切片，提升病理诊断准确率
CREATE-FFPE: Cross-Resolution Compensated and Multi-Frequency Enhanced FS-to-FFPE Stain Transfer for Intraoperative IHC Images
- 跨分辨率补偿+小波细节引导，解决图像污染与核结构模糊
- FID降低44.4%，KID降低71.2%，生成质量显著提升
- 适用于术中快速病理分析，可增强微卫星不稳定性预测
手术中免疫组化（IHC）分析依赖冰冻切片（FS）判断肿瘤良恶性，但其存在图像污染和核细节差的问题，影响病理诊断。相比之下，石蜡包埋（FFPE）图像染色质量高，但制备耗时长，无法用于术中。为此，本文提出首个面向术中IHC图像的FS-to-FFPE染色迁移框架CREATE-FFPE。针对上述问题，设计了跨分辨率补偿模块（CRCM）以多尺度补充组织信息，以及小波细节引导模块（WDGM）通过小波域生成精细细节并指导染色迁移。实验表明，该方法在自建数据集上优于所有对比方法；消融实验显示，加入CRCM与WDGM后FID下降44.4%，KID*100下降71.2%；且在公开数据集上的微卫星不稳定性预测任务性能显著提升。
原文摘要 · Abstract (English)
In the immunohistochemical (IHC) analysis during surgery, frozen-section (FS) images are used to determine the benignity or malignancy of the tumor. However, FS image faces problems such as image contamination and poor nuclear detail, which may disturb the pathologist's diagnosis. In contrast, formalin-fixed and paraffin-embedded (FFPE) image has a higher staining quality, but it requires quite a long time to prepare and thus is not feasible during surgery. To help pathologists observe IHC images with high quality in surgery, this paper proposes a Cross-REsolution compensATed and multi-frequency Enhanced FS-to-FFPE (CREATE-FFPE) stain transfer framework, which is the first FS-to-FFPE method for the intraoperative IHC images. To solve the slide contamination and poor nuclear detail mentioned above, we propose the cross-resolution compensation module (CRCM) and the wavelet detail guidance module (WDGM). Specifically, CRCM compensates for information loss due to contamination by providing more tissue information across multiple resolutions, while WDGM produces the desirable details in a wavelet way, and the details can be used to guide the stain transfer to be more precise. Experiments show our method can beat all the competing methods on our dataset. In addition, the FID has decreased by 44.4%, and KID*100 has decreased by 71.2% by adding the proposed CRCM and WDGM in ablation studies, and the performance of a downstream microsatellite instability prediction task with public dataset can be greatly improved by performing our FS-to-FFPE stain transfer.
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