A systematic review of semantic segmentation methods for histopathology images: a focused survey on breast, colon, and prostate cancers

Cancer, a non-communicable disease in which abnormal cells grow uncontrollably, harms and disrupts normal body functions, often leading to severe health complications and, if untreated, can be fatal. Amongst the non-communicable cancers, breast, colon, and prostate cancer are found to be prevalent as one of the top-ranking cancers recognized by the World Health Organization (WHO). Semantic segmentation is one of the useful methods widely used in histopathology segmentation and was found superior typically in digital pathology for grading purposes. Here, this study aims to provide a comprehensive systematic review, offering a broad overview of different semantic segmentation methods, focusing on breast, colon, and prostate cancers by using histopathology images. This study is meant to review research articles from the past decade: 2015–2024 compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Based on the proposed search strategy, a total of 43 articles were included for synthesis activity. The findings from this study reveal patterns, networks, relationships, and trends in the methods of semantic segmentation in breast, colon, and prostate cancers in the past decade. The findings of this study could be valuable for both the research community and medical service providers. They offer insights into the progress and trends of semantic segmentation in breast, colon, and prostate cancers over the past decade. At the same time, the study helps identify research gaps, potential markets, and key advantages, while also highlighting future possibilities. Ultimately, it contributes to ongoing research by either deepening or broadening the understanding of this important topic.