Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2018  |  Volume : 9  |  Issue : 1  |  Page : 48

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

Department of Pathology, Stanford University, Stanford, CA 94305, USA

Correspondence Address:
Dr. Robert S Ohgami
Department of Pathology, Stanford University, 300 Pasteur Drive, L235, Stanford, CA 94305
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_56_18

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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.

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