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

TL;DR

BlurryScope is a low-cost, compact scanning microscope that uses deep learning to accurately classify HER2 scores from blurry images, matching high-end scanners' performance.

Contribution

We introduced BlurryScope, a cost-effective, portable microscopy device that combines continuous image acquisition with deep learning for automated tissue analysis.

Findings

Achieved 79.3% accuracy for 4-class HER2 classification.

Achieved 89.7% accuracy for 2-class HER2 classification.

Automated the entire workflow from scanning to scoring.

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.