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.

翻译：组织病理学在医学诊断中发挥着关键作用。与耗时较长的永久切片制备不同，冰冻切片制备速度显著更快，可在手术期间进行，此时需优化样本扫描时间。超分辨率技术允许以较低放大倍数成像样本，从而节省扫描时间。本文提出一种针对组织病理冰冻切片的超分辨率新方法，重点在于获得更优的失真度量，而非追求可能损害关键诊断信息的照片级逼真图像。我们的深度学习架构聚焦于学习插值图像与真实图像之间的误差，从而在保持关键图像细节的同时生成高分辨率图像，降低诊断误判风险。这一目标通过利用频域损失函数实现，为复杂高频分量的重建赋予更高权重。与现有方法相比，我们在结构相似性指数和峰值信噪比方面取得显著提升，同时揭示了低分辨率冰冻切片图像中丢失的细节——这些细节会影响病理学家的临床决策。该方法在实现更少扫描、更快速冰冻切片成像的同时，保持成像样本的高分辨率特性，具有巨大应用潜力。