Mitosis detection techniques in H&E stained breast cancer pathological images: A comprehensive review. (May 2021)

• Record Type: Journal Article
• Authors: Pan, Xipeng; Lu, Yinghua; Lan, Rushi; Liu, Zhenbing; Qin, Zujun; Wang, Huadeng; Liu, Zaiyi
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Breast cancer -- Mitosis -- Detection -- Machine learning -- Deep learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107038
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Quantifying mitosis in pathological sections is of great significance in the pathological diagnosis of breast cancer as it is used to evaluate the aggressiveness of the tumor and to provide more comprehensive and reliable information for accurate diagnosis and treatment. In this paper, we summarized the current mainstream methods of mitosis detection and divided them into four categories, namely traditional methods, deep learning methods, methods combining deep learning with traditional methods and other methods. Each method is introduced, and the performance indicators achieved by some of these methods and their results are discussed, compared, and evaluated. We summarize some solutions to the problem of positive and negative sample imbalance in mitotic datasets. Through the review of the research methods in this field, the existing methods of mitosis research in breast cancer are summarized, and the future developments are prospected. Graphical abstract: Highlights: We aim to provide a review of the mainstream methods of mitosis detection in H&E stained breast cancer pathological images. This paper focuses on various approaches of mitosis detection and compares the approaches. We summarize the methods to deal with the imbalance of positive and negative samples in mitosis detection. The current difficulties and future improvements are introduced.