Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

Abstract

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

Keywords: Artificial intelligence; computational pathology; digital pathology; image analysis; quantitative image analysis; whole-slide imaging.

Figure 1

Hematoxylin and eosin-stained image analyzed using the “cell detection” algorithm in QuPath (open-source tool). Nuclear segmentation, depicted as red outlines, was fragmented in the rightmost image by setting the noise reduction Gaussian filter σ = 1.0. Conversely, nuclear oversegmentation was achieved by setting σ = 2.5. Black arrows in the left image denote examples of single objects that consisted of multiple nuclei

Figure 2

Sample hematoxylin and eosin images obtained from six sources designated on the right depict different color attributes commonly encountered when viewing digital images of slides across laboratories or across imaging modalities

Figure 3

(a) A mouse xenograft tumor sample is stained with hematoxylin and Ki67 (DAB). (b) An enlarged region is shown where nuclei are stained blue and Ki67+ cells are brown. (c) A pathologist-trained random forest classifier is developed to identify tumor (green), stroma (blue), necrosis (red), and glass (gray). (d) The algorithm parameters are fine-tuned with the pathologist's input to optimize the nuclear segmentation and to define intensity thresholds to categorize the expression into four bins: 0+ (blue), 1+ (yellow), 2+ (orange), and 3+ (red)

Figure 4

Digital pathology image analysis in spatial context reveal biomarker and cell heterogeneity. (a) The inset digital slide with DAB-stained biomarker (brown) was analyzed. Cells identified in the analysis were plotted spatially as a dot plot and each cell “dot” color coded according to the optical density of DAB stain in that cell. Cells in “cooler” colors (blues and greens) have lower stain optical density compared to cells in “warmer” colors (yellow, orange, and red). (b) Tumor cells and immune cells (DAB-positive) identified by image analysis were plotted spatially and analyzed to quantify immune cells within 30 μm of tumor cells (proximal immune cells). Tumor cells are colored blue, proximal immune cells are colored red, and nonproximal immune cells are green. The distance between tumor cells and proximal immune cells are recorded to create a histogram (inset, bottom right) and are connected by nearest neighbor lines in the dot plot

Figure 5

Digital pathology image analysis in the pancreas and brain. (a) Islets stained with antibodies against insulin (red stain) and glucagon (brown stain) in digital slides. Analysis shown bottom left quantifies number of islets that are of islet (orange area in markup) and number of cells that are positive for insulin (red cell markup), glucagon (green cell markup), both (yellow cell markup), or neither marker (white cell markup). (b) Identification of beta-amyloid plaques in brain sections. Slides are probed with antibodies against beta-amyloid (purple) and vessel endothelial marker (brown). Digital image analysis shown bottom right quantifies density, diameter, and area of vessels (red markup) and plaques (green markup), and colocalized area (yellow markup)