Super-resolution digital pathology image processing of bone marrow aspirate and cytology smears and tissue sections

• Main Authors: Amol Singh, Robert S Ohgami
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2018-01-01
• Series: Journal of Pathology Informatics
• Subjects: Cytology; digital pathology; high resolution; super resolution
• Online Access: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2018;volume=9;issue=1;spage=48;epage=48;aulast=Singh

Background: Accurate digital pathology image analysis depends on high-quality images. As such, it is imperative to obtain digital images with high resolution for downstream data analysis. While hematoxylin and eosin (H&E)-stained tissue section slides from solid tumors contain three-dimensional information, these data have been ignored in digital pathology. In addition, in cytology and bone marrow aspirate smears, the three-dimensional nature of the specimen has precluded efficient analysis of such morphologic data. An individual image snapshot at a single focal distance is often not sufficient for accurate diagnoses and multiple whole-slide images at different focal distances are necessary for diagnostics. Materials and Methods: We describe a novel computational pipeline and processing program for obtaining a super-resolved image from multiple static images at different z-planes in overlapping but separate frames. This program, MULTI-Z, performs image alignment, Gaussian smoothing, and Laplacian filtering to construct a final super-resolution image from multiple images. Results: We applied this algorithm and program to images of cytology and H&E-stained sections and demonstrated significant improvements in both resolution and image quality by objective data analyses (24% increase in sharpness and focus). Conclusions: With the use of our program, super-resolved images of cytology and H&E-stained tissue sections can be obtained to potentially allow for more optimal downstream computational analysis. This method is applicable to whole-slide scanned images.