Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms.

Journal: Pathologie (Heidelberg, Germany)
PMID: 39570395

Abstract

BACKGROUND: Histomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to deduce the spatial properties of structures. Micromorphometric parameters are calculated from these sections based on the assumption of a plate-like 3D microarchitecture, resulting in the loss of 3D structure due to the destructive nature of classical histological processing.

Authors

  • J Báskay
    Department of Biological Physics, Eötvös Loránd University, Pázmány Péter Sétány 1/a, 1117, Budapest, Hungary.
  • M Kivovics
    Department of Community Dentistry, Semmelweis University, Szentkirályi Utca 40, 1088, Budapest, Hungary.
  • D Pénzes
    Department of Community Dentistry, Semmelweis University, Szentkirályi Utca 40, 1088, Budapest, Hungary.
  • E Kontsek
    Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary. kontsek.endre@semmelweis.hu.
  • A Pesti
    Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary.
  • A Kiss
    Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary.
  • M Szócska
    Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University, Kútvölgyi út 2, 1125, Budapest, Hungary.
  • O Németh
    Department of Community Dentistry, Semmelweis University, Szentkirályi Utca 40, 1088, Budapest, Hungary.
  • P Pollner
    Department of Biological Physics, Eötvös Loránd University, Pázmány Péter Sétány 1/a, 1117, Budapest, Hungary.

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