KEYWORDS: In vivo imaging, Tissues, Windows, Animal model studies, Nonlinear optics, Microlens, Optical microscopy, Histopathology, Tissue optics, Signal generators
Tissue histopathology, reliant on costly and time-consuming hematoxylin and eosin (H&E) staining of thin tissue slices, faces limitations. Label-free non-linear optical microscopy in vivo presents a solution, allowing work on fresh samples. Implantable microstructures prove effective for systematic longitudinal in vivo studies of immunological responses to biomaterials using label-free non-linear optical microscopy. Employing two-photon laser polymerization, we implanted a matrix of 3D lattices in the chorioallantoic membrane of chicken embryos, establishing a 3D reference frame for cell counting. H&E analysis is compared to label-free in vivo non-linear excitation imaging for cell quantification and identifying granulocytes, collagen, and microvessels. Preliminary results in higher animal models demonstrate the transformative potential of this approach, offering an alternative to conventional histopathology for validating biomaterials in in vivo longitudinal studies.
H&E stained sections are the gold standard for disease diagnosis but, unfortunately, the staining process is time-consuming and expensive. In an effort to overcome these problems, here, we propose a virtual staining algorithm, able to predict an Hematoxylin/Eosin (H&E) image, usually exploited during clinical evaluations, starting from the autofluorescence signal of entire liver tissue sections acquired by a confocal microscope. The color and texture contents of the generated virtually stained images have been analyzed through the phasor-based approach to detect tumorous tissue and to segment relevant biological structures (accuracy>90% compared to the expert manual analysis).
We present a new AI-based method for the quantification of liver fibrosis in tissue sections stained with Picro Sirius Red which highlights collagen. The method segments and quantifies collagen, a marker of the fibrotic response, through a deep learning model trained on 20 whole-slide images. The results show a Dice score > 90% compared to manual annotations, demonstrating its potential aid during diagnosis. Furthermore, our approach can be extended to other staining protocols.
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