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arXiv · 2410.17557

BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images

Michael John Fanous·Christopher Michael Seybold·Hanlong Chen·Nir Pillar·Aydogan Ozcan
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Abstract

We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point and smaller size/weight. Using BlurryScope, we implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on motion-blurred images of immunohistochemically (IHC) stained breast tissue sections, achieving concordant results with those obtained from a high-end digital scanning microscope. Using a test set of 284 unique patient cores, we achieved testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0/1+, 2+/3+) HER2 classification, respectively. BlurryScope automates the entire workflow, from image scanning to stitching and cropping, as well as HER2 score classification.

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Michael John Fanous, Christopher Michael Seybold, Hanlong Chen, Nir Pillar, Aydogan Ozcan. 2024-10-23. BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images. https://doi.org/10.1038/s41746-025-01882-x

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