Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention

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.

Super resolution of histopathological frozen sections via deep learning preserving tissue structure

Histopathology plays a pivotal role in medical diagnostics. In contrast to preparing permanent sections for histopathology, a time-consuming process, preparing frozen sections is significantly faster and can be performed during surgery, where the sample scanning time should be optimized. Super-resolution techniques allow imaging the sample in lower magnification and sparing scanning time. In this paper, we present a new approach to super resolution for histopathological frozen sections, with focus on achieving better distortion measures, rather than pursuing photorealistic images that may compromise critical diagnostic information. Our deep-learning architecture focuses on learning the error between interpolated images and real images, thereby it generates high-resolution images while preserving critical image details, reducing the risk of diagnostic misinterpretation. This is done by leveraging the loss functions in the frequency domain, assigning higher weights to the reconstruction of complex, high-frequency components. In comparison to existing methods, we obtained significant improvements in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as indicated details that lost in the low-resolution frozen-section images, affecting the pathologist's clinical decisions. Our approach has a great potential in providing more-rapid frozen-section imaging, with less scanning, while preserving the high resolution in the imaged sample.