Computational methods for automated mitosis detection in histopathology images: A review

• Autorzy: Mathew Tojo , Kini Jyoti R. , Rajan Jeny

Identyfikatory

DOI

10.1016/j.bbe.2020.11.005

• Języki publikacji: EN

Abstrakty

EN

Mitosis detection is an important step in pathology procedures in the context of cancer diagnosis and prognosis. Prevalent process for this task is by manually observing Hematox-ylin and Eosin (H & E) stained histopathology sections on glass slides through a microscope by trained pathologists. This conventional approach is tedious, error-prone, and has shown high inter-observer variability. With the advancement of computational technologies, automating mitosis detection by the use of image processing algorithms has attracted significant research interest. In the past decade, several methods appeared in the literature, addressing this problem and they have shown encouraging incremental progress towards a clinically usable solution. Mitosis count is an important parameter in grading of breast cancer and glioma, unlike other cancer types. Driven by the availability of multiple public datasets and open contests, most of the methods in literature address mitosis detection in breast cancer images. This paper is a comprehensive review of the methods published in the area of automated mitotic cell detection in H & E stained histopathology images of breast cancer in the last 10 years. We also discuss the current trends and future prospects of this clinically relevant task, augmenting humanity's fight against cancer.

Słowa kluczowe

EN

mitosis detection; histopathology; breast cancer grading; deep learning; digital pathology

PL

wykrywanie mitozy; histopatologia; nowotwór piersi; uczenie głębokie; patologia cyfrowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 64--82
• Opis fizyczny: Bibliogr. 132 poz., rys., tab., wykr.

Twórcy

autor

Mathew Tojo

• Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India; Department of Computer Science and Engineering, The National Institute of Engineering, Mysuru, India

autor

Kini Jyoti R.

• Department of Pathology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India

autor

Rajan Jeny

• Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).