Digital Pathology: Data-Intensive Frontier in Medical Imaging: Health-information sharing, specifically of digital pathology, is the subject of this paper which discusses how sharing the rich images in pathology can stretch the capabilities of all otherwise well-practiced disciplines

Abstract

Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.

Keywords: Biomedical imaging; biomedical informatics; digital pathology; image analysis; virtual microscopy.

Fig. 1

Whole slide imaging (WSI). (a) WSI captures the contents of an entire glass slide at high magnification, producing an image containing billions of pixels. (b) Detail view of (a). A hematoxylin and eosin (H&E) stain highlights structure. (c) Immunohistochemical staining tags specific proteins with a colorimetric signal. Here, a protein expressed by oxygen-deprived cells is marked with dark brown.

Fig. 2

National Library of Medicine index articles show increasing trends in pathology digitization and image analysis.

Fig. 3

Slide production estimates. Anaverage sized pathology practice can reasonably produce 80 000 slides per year. (*)Time available to perform WSI scanning using a single WSI scanner.

Fig. 4

Morphological analysis of glioblastoma. (a) Two hundred million nuclei corresponding to 167 patients were segmented and analyzed to (b) generate statistical models of features that represent patient morphology. (c) Clustering of 167 patient models revealed four groups with distinct morphology. (d) Kaplan Meier plots of patient survival show significant differences between morphology-driven patient groups.