Accurate diagnosis achieved via super-resolution whole slide images by pathologists and artificial intelligence

Abstract

Background: Digital pathology significantly improves diagnostic efficiency and accuracy; however, pathological tissue sections are scanned at high resolutions (HR), magnified by 40 times (40X) incurring high data volume, leading to storage bottlenecks for processing large numbers of whole slide images (WSIs) for later diagnosis in clinic and hospitals. Method: We propose to scan at a magnification of 5 times (5X). We developed a novel multi-scale deep learning super-resolution (SR) model that can be used to accurately computes 40X SR WSIs from the 5X WSIs. Results: The required storage size for the resultant data volume of 5X WSIs is only one sixty-fourth (less than 2%) of that of 40X WSIs. For comparison, three pathologists used 40X scanned HR and 40X computed SR WSIs from the same 480 histology glass slides spanning 47 diseases (such tumors, inflammation, hyperplasia, abscess, tumor-like lesions) across 12 organ systems. The results are nearly perfectly consistent with each other, with Kappa values (HR and SR WSIs) of 0.988±0.018, 0.924±0.059, and 0.966±0.037, respectively, for the three pathologists. There were no significant differences in diagnoses of three pathologists between the HR and corresponding SR WSIs, with Area under the Curve (AUC): 0.920±0.164 vs. 0.921±0.158 (p-value=0.653), 0.931±0.128 vs. 0.943±0.121 (p-value=0.736), and 0.946±0.088 vs. 0.941±0.098 (p-value=0.198). A previously developed highly accurate colorectal cancer artificial intelligence system (AI) diagnosed 1,821 HR and 1,821 SR WSIs, with AUC values of 0.984±0.016 vs. 0.984±0.013 (p-value=0.810), again with nearly perfect matching results. Conclusions: The pixel numbers of 5X WSIs is only less than 2% of that of 40X WSIs. The 40X computed SR WSIs can achieve accurate diagnosis comparable to 40X scanned HR WSIs, both by pathologists and AI. This study provides a promising solution to overcome a common storage bottleneck in digital pathology.