Our study introduces a label-free imaging and quantitative analysis approach for investigating lipofuscin aggregates in human brain tissue. Leveraging the colocalization of lipofuscin with cell soma, our novel method accurately identifies and counts cells, especially large neurons. Achieving an impressive 92% accuracy at submicron resolution, our label-free approach outperforms the commonly used Nissl stain. We develop a robust segmentation technique for lipofuscin aggregates, revealing layered structures in the cortical gray matter, potentially associated with cell distribution. Furthermore, we validate our results using state-of-the-art techniques, including fluorescence lifetime imaging microscope and sub-micron resolution two photon imaging. Our findings contribute valuable insights into neurodegenerative diseases and hold promise for future diagnostic advancements.
Cerebral microvascular alterations are increasingly recognized as important contributors to neurodegenerative diseases, such as Alzheimer's Disease (AD) and chronic traumatic encephalopathy (CTE). To characterize these changes, we used a custom serial-sectioning, polarization-sensitive optical coherence tomography system to collect volumetric images of brain tissue volumes from the dorsolateral frontal lobe of normal controls and subjects with pathologically confirmed AD and CTE. We performed vasculature segmentation on each tissue volume and then analyzed the geometric properties of the vascular networks. The preliminary results suggest reduced microvascular density in AD compared to both CTE and NC.
The signal of optical coherence tomography (OCT) decays exponentially in depth due to tissue scattering, resulting in indistinct tissue features in three-dimension. Moreover, due to limited light penetration depth, extensive volumetric investigation is usually constrained for large-scale biological samples. By integrating serial sectioning technology with block-face imaging, we establish a volumetric OCT acquisition and reconstruction pipeline that incorporates depth-resolved attenuation coefficient estimation, volumetric stitching and filtering, and feature enhancement visualization. We demonstrate this pipeline on ex vivo human brain volumes of several cubic centimeters with 5 um isotropic resolution.
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