Yoshai Elad, Goldinger Gil, Kogan Tatiana, Zakharov Anna, Haifler Miki, Shaked Natan T
School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
Chaim Sheba Medical Center, Ramat Gan, Israel.
Sci Rep. 2025 Aug 20;15(1):30594. doi: 10.1038/s41598-025-12181-6.
In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei region. Permanent sections, on the other hand, contain more diagnostic detail but require a time-intensive preparation process. Here, we present a generative deep learning approach to enhance frozen section images by leveraging guidance from permanent sections. Our method places a strong emphasis on the nuclei region, which contains critical information in both frozen and permanent sections. Importantly, our approach avoids generating artificial data in blank regions, ensuring that the network only enhances existing features without introducing potentially unreliable information. We achieve this through a segmented attention network, incorporating nuclei-segmented images during training and adding an additional loss function to refine the nuclei details in the generated permanent images. We validated our method across various tissues, including kidney, breast, and colon. This approach significantly improves histological efficiency and diagnostic accuracy, enhancing frozen section images within seconds, and seamlessly integrating into existing laboratory workflows.
在组织病理学中，冰冻切片常用于手术期间的快速诊断，因为它们能在几分钟内制作出来。然而，它们存在伪像，并且常常缺乏关键的诊断细节，尤其是在细胞核区域。另一方面，永久切片包含更多的诊断细节，但需要耗时的制备过程。在此，我们提出一种生成式深度学习方法，通过利用永久切片的指导来增强冰冻切片图像。我们的方法高度重视细胞核区域，该区域在冰冻切片和永久切片中都包含关键信息。重要的是，我们的方法避免在空白区域生成人工数据，确保网络仅增强现有特征而不引入潜在不可靠的信息。我们通过一个分割注意力网络来实现这一点，在训练期间纳入细胞核分割图像，并添加一个额外的损失函数来细化生成的永久图像中的细胞核细节。我们在包括肾脏、乳腺和结肠在内的各种组织上验证了我们的方法。这种方法显著提高了组织学效率和诊断准确性，能在几秒钟内增强冰冻切片图像，并无缝集成到现有的实验室工作流程中。
