Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model.
Zhijie LiuLingchao ChenHaixia ChengJianpeng AoJi XiongXing LiuYaxin ChenYing MaoMinbiao JiPublished in: Science advances (2024)
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.
Keyphrases
- deep learning
- optical coherence tomography
- nucleic acid
- convolutional neural network
- label free
- decision making
- raman spectroscopy
- white matter
- patients undergoing
- endothelial cells
- resting state
- machine learning
- protein protein
- magnetic resonance
- electronic health record
- high throughput
- amino acid
- magnetic resonance imaging
- small molecule
- multiple sclerosis
- flow cytometry
- big data
- mass spectrometry
- high speed
- blood brain barrier
- data analysis
- sensitive detection