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

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

Automated Whole Slide Imaging for Label-Free Histology Using Photon Absorption Remote Sensing Microscopy 789 444 IEEE Transactions on Biomedical Engineering (TBME)
Author(s): James E.D. Tweel; Benjamin R. Ecclestone; Marian Boktor; Deepak Dinakaran; John R. Mackey; Parsin Haji Reza

Pathologists rely on histochemical stains to impart contrast in thin translucent tissue samples, revealing tissue features necessary for identifying pathological conditions. Unfortunately, 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 develop a label-free whole slide scanner using Photon Absorption Remote Sensing (PARS) microscopy, allowing for imaging of thin, transmissible tissue samples without chemical staining.

Peak signal-to-noise ratio 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. PARS WSIs are presented at standard 40x magnification in malignant human breast and skin samples. Identical tissue sections are subsequently H&E stained and brightfield imaged. The one-to-one WSIs from both modalities are visually and quantitatively compared. Correspondence of subcellular diagnostic details is shown between both PARS and H&E WSIs and virtual H&E staining of an entire PARS WSI is demonstrated. The one-to-one WSI from both modalities show quantitative similarity in nuclear features and structural information.

The PARS WSIs are compatible with existing digital pathology tools, and samples remain suitable for histochemical, immunohistochemical, and other staining techniques. This work is a critical advance for integrating label-free optical methods into standard histopathology workflows.

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