Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid–/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors.

中文翻译：

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通过刺激拉曼 CycleGAN 模型对新鲜脑组织进行虚拟福尔马林固定和石蜡包埋染色

术中组织学对于手术指导和决策至关重要。然而，冰冻切片苏木精和伊红 (H&E) 染色的准确性较低，而金标准福尔马林固定石蜡包埋 (FFPE) H&E 的长度太长，不适合术中使用。受激拉曼散射 (SRS) 显微镜已通过脂质/蛋白质对比显示了脑组织的快速组织学，但难以生成与病理学家可解释的基于核酸/蛋白质的 FFPE 染色相同的图像。在这里，我们报告了半监督受激拉曼 CycleGAN 模型的开发，该模型使用不成对的训练数据将新鲜组织 SRS 图像转换为 H&E 染色。 3 分钟内，即可生成与真实 H&E 完美匹配的受激拉曼虚拟组织学 (SRVH) 结果。盲法验证表明，经过委员会认证的神经病理学家能够在 SRVH 上区分人类神经胶质瘤的组织学亚型，但在传统的 SRS 图像上却很难区分。 SRVH 可在速度和准确性方面提供优于冷冻 H&E 的术中诊断，并可扩展到其他类型的实体瘤。