Current Landscape of Advanced Imaging Tools for Pathology Diagnostics.

Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.

Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining.

Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the process is laborious and time-consuming, often limiting its usage in crucial applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback in vivo, this deep learning-enabled qOBM approach could enable new workflows for histopathology with the potential to significantly save time, labor, and costs in cancer screening, detection, treatment guidance, and more.

Pilot study of fluorescence imitating brightfield imaging for rapid, slide-free dermatopathology.

BACKGROUND: Fluorescence imitating brightfield imaging (FIBI) is a novel alternative microscopy method that can image freshly excised, non-sectioned tissue. We examine its potential utility in dermatopathology by examining readily available specimens embedded in paraffin blocks. METHODS: Nine skin samples embedded in paraffin blocks were superficially deparaffinized using xylene and ethanol and stained with H&E. FIBI captured tissue surface histopathology images using simple microscope optics and a color camera. We then applied deep-learning-based models to improve resemblance to standard H&E coloration and contrast. FIBI images were compared with corresponding standard H&E slides and concordance was assessed by two dermatopathologists who numerically scored epidermal and dermal structure appearance and overall diagnostic utility. RESULTS: Dermatopathologist scores indicate that FIBI images are at least equivalent to standard H&E slides for visualizing structures such as epidermal layers, sweat glands, and nerves. CONCLUSION: Images acquired with FIBI are comparable to traditional H&E-stained slides, suggesting that this rapid, inexpensive, and non-destructive microscopy technique is a conceivable alternative to standard histopathology processes especially for time-sensitive procedures and in settings with limited histopathology resources.