Automated Whole Slide Imaging for Label-Free Histology Using Photon Absorption Remote Sensing Microscopy

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Abstract

Objective: Pathologists rely on histochemical stains to impart contrast in thin translucent tissue samples, revealing tissue features necessary for identifying pathological conditions. However, the chemical labeling process is destructive and often irreversible or challenging to undo, imposing practical limits on the number of stains that can be applied to the same tissue section. Here we present an automated label-free whole slide scanner using a PARS microscope designed for imaging thin, transmissible samples. Methods: Peak SNR and in-focus acquisitions are achieved across entire tissue sections using the scattering signal from the PARS detection beam to measure the optimal focal plane. Whole slide images (WSI) are seamlessly stitched together using a custom contrast leveling algorithm. Identical tissue sections are subsequently H&E stained and brightfield imaged. The one-to-one WSIs from both modalities are visually and quantitatively compared. Results: PARS WSIs are presented at standard 40x magnification in malignant human breast and skin samples. We show correspondence of subcellular diagnostic details in both PARS and H&E WSIs and demonstrate virtual H&E staining of an entire PARS WSI. The one-to-one WSI from both modalities show quantitative similarity in nuclear features and structural information. Conclusion: PARS WSIs are compatible with existing digital pathology tools, and samples remain suitable for histochemical, immunohistochemical, and other staining techniques. Significance: This work is a critical advance for integrating label-free optical methods into standard histopathology workflows.

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APA

Tweel, J. E. D., Ecclestone, B. R., Boktor, M., Dinakaran, D., Mackey, J. R., & Reza, P. H. (2024). Automated Whole Slide Imaging for Label-Free Histology Using Photon Absorption Remote Sensing Microscopy. IEEE Transactions on Biomedical Engineering, 71(6), 1901–1912. https://doi.org/10.1109/TBME.2024.3355296

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