Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging

Abstract

Histopathological examination of tissue sections is the gold standard for disease diagnosis. However, the conventional histopathology workflow requires lengthy and laborious sample preparation to obtain thin tissue slices, causing about a one-week delay to generate an accurate diagnostic report. Recently, microscopy with ultraviolet surface excitation (MUSE), a rapid and slide-free imaging technique, has been developed to image fresh and thick tissues with specific molecular contrast. Here, we propose to apply an unsupervised generative adversarial network framework to translate colorful MUSE images into Deep-MUSE images that highly resemble hematoxylin and eosin staining, allowing easy adaptation by pathologists. By eliminating the needs of all sample processing steps (except staining), a MUSE image with subcellular resolution for a typical brain biopsy (5 mm × 5 mm) can be acquired in 5 minutes, which is further translated into a Deep-MUSE image in 40 seconds, simplifying the standard histopathology workflow dramatically and providing histological images intraoperatively.

Deep learning-enabled virtual histological staining of biological samples

“…Adapted with permission from ref. 60 © The Optical Society demonstrated by several research groups 21,[47][48][49][50][51][52][53][54][55][56] . In the work of de Haan et al (Fig.…”

Rapid and label-free histological imaging of unprocessed surgical tissues via dark-field reflectance ultraviolet microscopy

“…Microscopy with ultraviolet surface excitation (MUSE) uses the shallow penetration depth of ultraviolet light to achieve moderate optical sectioning. 3 , 4 , 5 Structured illumination microscopy (SIM) rejects out-of-focus background digitally by leveraging the fact that only in-focus components can be modulated by structured illumination. 6 , 7 , 8 Light-sheet microscopy (LSM) achieves optical sectioning by a thin “selective” illumination plane and the signal collection from the orthogonal direction.…”

Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning

“…Recent years have seen rapid advances in deep learning-based virtual staining techniques, providing promising alternatives to the traditional histochemical staining workflow by computationally staining the microscopic images captured from label-free thin tissue sections, bypassing the laborious and costly chemical staining process. Such label-free virtual staining techniques have been demonstrated using autofluorescence imaging [9,10], quantitative phase imaging [11], and light scattering imaging [12], among others [13][14][15], and have successfully created multiple types of histochemical stains, e.g., hematoxylin and eosin (H&E) [9][10][11][12][13][14], Masson's trichrome [9][10][11], and Jones silver stains [9][10][11]. These previous works did not perform any virtual IHC staining and mainly focused on the generation of structural tissue staining, which enhances the contrast of specific morphological features in tissue sections.…”