Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence...

Area of Science:

• Neuroscience
• Biomedical Imaging
• Computational Biology

Background:

• Visualizing human brain structure across scales is challenging.
• Traditional histology methods face limitations like staining variability and tissue distortion.
• Label-free serial sectioning optical coherence tomography (S-OCT) offers 3D imaging but lacks histological interpretability.

Purpose of the Study:

• To develop a novel 3D imaging framework integrating S-OCT with deep-learning digital staining (DS).
• To improve the interpretability and reduce variability in S-OCT brain imaging.
• To enable high-throughput, multiscale 3D histology studies of brain structures.

Main Methods:

• Combined S-OCT with a deep-learning digital staining (DS) model.
• Developed a semi-supervised learning technique for DS model training.
• Translated S-OCT images to Gallyas silver staining using the DS model.

Main Results:

• Achieved consistent digital staining quality across human cerebral cortex samples.
• Enhanced contrast across cortical layer boundaries.
• Preserved 3D geometry for visualizing meso-scale white matter vessel networks.

Conclusions:

• The novel framework integrates high-throughput 3D imaging with low sample variability and high interpretability.
• Digital staining of S-OCT data facilitates accurate 3D histology.
• This technique holds potential for advancing multiscale brain tissue imaging and structural studies.