Introduction to digital pathology and computer-aided pathology

Abstract

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

Keywords: Artificial intelligence; Computer-aided pathology; Deep learning; Digital pathology.

Fig. 1.

Under- (A) and over-nuclear (B) nuclear segmentation. Under- and over-nuclear segmentation results in ImageJ (open-source tool). Immunohistochemical staining images for Ki-67 antibody shows nuclear presentation. As the settings for range of diameter, area eccentricity and staining intensity change, segmentation algorithm shows different results. (A) algorithm failed to detect some positive nuclei. (B) algorithm showed that too many small non-specific stains were counted in the positive nucleus, resulting in over-nuclear counting. As a result, the count of (A) algorithm was 88, and the count of (B) algorithm was 130.

Fig. 2.

Various hematoxylin and eosin (H&E) staining color attributes. (A–F) Six H&E staining images for colonic mucosa represent color properties that usually occur in various laboratory settings or image scanning methods. Panels A and D were stained in the same laboratory.