- Tytuł:
- 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. - Powiązania:
- https://bibliotekanauki.pl/articles/202412.pdf
- Data publikacji:
- 2018
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
damaged tissue regions detection
artifacts detection
deep learning
Ki-67 staining specimens
wykrywanie uszkodzonych regionów tkankowych
wykrywanie artefaktów
artefakt
uczenie głębokie
próbki barwiące Ki-67 - Opis:
- 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.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 849-856
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł