Digital Microscopy, Image Analysis, and Virtual Slide Repository

Abstract

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Keywords: deep learning; image analysis; slide repository; slide scanner; stereology; virtual microscopy; whole-slide imaging; whole-slide scanning.

Figure 1

Scanning patterns. (A) Tile scanning of every tile. The arrows indicate direction of scanning (modified after Indu et al. 2016). Dots within a tile indicate a focus point. (B) Tile scanning of every nth field. (C) Line scanning pattern. Dots indicate focus points of a focus map.

Figure 2

Assessing multiple characteristics of the same specimen simultaneously in digital slide view. A display of digital slide viewer depicting side-by-side viewing of H&E stain (A) and different immunohistochemical markers (B–D) on serial sections of a mouse prostate tumor. Primary antibody chromogenic detection with DAB and hematoxylin counterstain (B–D). Inset rectangle at the bottom right of each of the four displays shows a full slide overview; the small rectangle within each inset (arrow example) shows the region that is displayed at higher power magnification (10× in this case). Glass slides were scanned at 40× magnification with NanoZoomer slide scanner and viewed with NDP viewing software (Hamamatsu Photonics, Bridgewater NJ, USA).

Figure 3

Simplified schematic of deep learning approach. Example images of epithelial cells and other cells are fed into a convolution layer that passes on features to a neural network to learn to identify epithelial cells autonomously in unknown images. As the information is passing through the neural network (comprised of connected computing units called “neurons” arranged in layers), the algorithm freely selects the most important image features of epithelial cells. Entering these layers of neurons, this feature information is transformed and passed on through weighted connections to the next layer. The algorithm completes thousands of training cycles to learn to recognize these structures autonomously in unknown images.

Figure 4

Examples of utilization of digital image analysis as research tools. (A) Example of area-based assessment in quantification of cardiac fibrosis (⁴⁴). In the markup image, blue areas are those of cardiac fibrosis, red areas are those comprised of cardiomyocytes, and yellow is empty slide area. An algorithm can extract exact quantification of the size of each surface area. (B) Example of utilization of nuclear orientation in the identification of different glandular structures (⁷⁶). The lines overlaying the nuclei are generated by an algorithm and represent the axis of each nucleus along its major diameter (largest width). Analysis of the relationship of these axes to each other enabled researchers to separate glandular structures (more parallel axis orientation) from other tissue components. (C) Example of cell-based assessment in the separation of stromal cells (middle panel) and epithelial cells (right panel) to quantify positive cells based upon nuclear IHC signal (⁷⁵). Extraction of counts for each tissue compartment is possible via algorithm but also of morphological features (e.g., size, roundness, major diameter, etc.) for every individual cell analyzed here. Green cells = not selected; blue cells = target cell population, IHC negative; red cells = target cell population, biomarker positive.