Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model

• Autorzy: Swiderska-Chadaj Z. , Markiewicz T. , Gallego J. , Bueno G. , Grala B. , Lorent M.

Identyfikatory

DOI

10.24425/bpas.2018.125932

• Języki publikacji: EN

Abstrakty

EN

The pathologists follow a systematic and partially manual process to obtain histological tissue sections from the biological tissue extracted from patients. This process is far from being perfect and can introduce some errors in the quality of the tissue sections (distortions, deformations, folds and tissue breaks). In this paper, we propose a deep learning (DL) method for the detection and segmentation of these damaged regions in whole slide images (WSIs). The proposed technique is based on convolutional neural networks (CNNs) and uses the U-net model to achieve the pixel-wise segmentation of these unwanted regions. The results obtained show that this technique yields satisfactory results and can be applied as a pre-processing step for automatic WSI analysis in order to prevent the use of the damaged areas in the evaluation processes.

Słowa kluczowe

EN

damaged tissue regions detection; artifacts detection; deep learning; Ki-67 staining specimens

PL

wykrywanie uszkodzonych regionów tkankowych; wykrywanie artefaktów; artefakt; uczenie głębokie; próbki barwiące Ki-67

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2018
• Tom: Vol. 66, nr 6
• Strony: 849--856
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Swiderska-Chadaj Z.

• Warsaw University of Technology, Faculty of Electrical Engineering, Warsaw, Poland

autor

Markiewicz T.

• Warsaw University of Technology, Faculty of Electrical Engineering, Warsaw, Poland
• Military Institute of Medicine, Department of Pathomorphology, Warsaw, Poland

autor

Gallego J.

• VISILAB Group, Universidad de Castilla-La Mancha, E.T.S.I.I, Ciudad Real, Spain

autor

Bueno G.

• VISILAB Group, Universidad de Castilla-La Mancha, E.T.S.I.I, Ciudad Real, Spain

autor

Grala B.

• Military Institute of Medicine, Department of Pathomorphology, Warsaw, Poland
• VISILAB Group, Universidad de Castilla-La Mancha, E.T.S.I.I, Ciudad Real, Spain

autor

Lorent M.

• Military Institute of Medicine, Department of Pathomorphology, Warsaw, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).