A hybrid deep learning approach for gland segmentation in prostate histopathological images.

Journal: Artificial intelligence in medicine
Published Date: Apr 16, 2021

Abstract

BACKGROUND: In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the presence of cancer tissue. The manual annotations are operator-dependent, error-prone and time-consuming. The automated segmentation of prostate glands can be very challenging too due to large appearance variation and serious degeneration of these histological structures.

Authors

  • Massimo Salvi
  • Martino Bosco
    San Lazzaro Hospital, Department of Pathology, Via Petrino Belli 26, Alba, 12051, Italy.
  • Luca Molinaro
    A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy.
  • Alessandro Gambella
    A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy.
  • Mauro Papotti
    University of Turin, Division of Pathology, Department of Oncology, Via Santena 5, Turin, 10126, Italy.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.
  • Filippo Molinari
    Department of Electronics and Telecommunications, Politecnico di Torino, Italy.

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