AI Insight
Researchers developed a multimodal microscope combining quantitative oblique back-illumination microscopy (qOBM) with fluorescence microscopy to image fresh, unprocessed tissue in three dimensions. They trained an artificial intelligence model to predict DAPI-like nuclear staining from phase contrast images alone, eliminating the need for actual fluorescent dyes while achieving improved imaging depth and uniformity. The technique also generates virtual H&E staining of thick tissue samples, potentially enabling histological analysis without traditional chemical processing.
Why it matters
This technology could transform surgical pathology by allowing real-time tissue analysis during operations without lengthy sample preparation, potentially improving surgical decision-making. The ability to perform virtual staining on fresh, unprocessed tissue may also benefit biological research by preserving tissue integrity while still obtaining detailed structural information comparable to traditional histology.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
We introduce a multimodal microscope that combines quantitative oblique back-illumination microscopy (qOBM) with wide-field epi-fluorescence deconvolution microscopy to acquire intrinsically co-registered phase and fluorescence volumes of fresh, bulk tissue. Using paired qOBM phase and DAPI fluorescence images of fresh ex vivo mouse brain tissue, we train a supervised conditional generative adversarial network (cGAN) to predict DAPI-like nuclear contrast from the structural information encoded in the phase images. Results not only demonstrate high-fidelity image translation but also show improved imaging depth and more homogeneous staining. Additionally, we combine the qOBM phase contrast with captured or virtual fluorescence nuclear images to produce volumetric pseudo-H&E representations of fresh tissue, enabling a supervised approach for achieving robust virtual histological staining. This work paves the way for virtual fluorescence imaging of many more structural targets and supervised virtual histological staining of thick, unaltered, bulk tissues ex vivo and potentially in vivo. Such capabilities could have significant implications for biological research as well as for point-of-care, intraoperative, and other clinical applications.