Three-dimensional visualization of blood vessels in human gliomas based on tissue clearing and deep learning

Abstract

AbstractGliomas, with their intricate and aggressive nature, call for a detailed visualization of their vasculature. While many studies lean towards 2D imaging of thin sections, this method often overlooks the full spatial heterogeneity inherent to tumors. To overcome this limitation, our study melded state-of-the-art techniques, encompassing tissue clearing technology, 3D confocal microscopy imaging, and deep learning-aided vessel extraction, resulting in a comprehensive 3D visualization of glioma vasculature in intact human tissue. Specifically, we treated formalin-fixed thick human glioma tissue sections (500 μ m) with OPTIClear for transparency and subsequently performed immunofluorescent labeling using CD31. Using confocal microscopy, we obtained 3D images of the glioma vasculature. For vessel extraction, we employed a specialized 3D U-Net, enriched with image preprocessing and post-processing methods, and benchmarked its performance against the Imaris software. Our findings indicated that OPTIClear-enabled tissue clearing yielded a holistic 3D representation of immunolabeled vessels in clinical human glioma samples. Impressively, our deep learning technique outshined the traditional Imaris approach in terms of accuracy and efficiency in vessel extraction. Further, discernible variations in vascular metrics, such as mean diameter, branching point count, and volume ratio, were observed between low-grade and high-grade gliomas. In essence, our innovative blend of tissue clearing and deep learning not only enables enhanced 3D visualization of human glioma vasculature but also underscores morphological disparities across glioma grades, potentially influencing pathological grading, therapeutic strategies, and prognostic evaluations.