A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
H&E image analysis pipeline for quantifying morphological features
Tekijät: Ariotta Valeria, Lehtonen Oskari, Salloum Shams, Micoli Giulia, Lavikka Kari, Rantanen Ville, Hynninen Johanna, Virtanen Anni, Hautaniemi Sampsa
Kustantaja: Elsevier BV
Julkaisuvuosi: 2023
Journal: Journal of Pathology Informatics
Tietokannassa oleva lehden nimi: Journal of pathology informatics
Lehden akronyymi: J Pathol Inform
Artikkelin numero: 100339
Vuosikerta: 14
eISSN: 2153-3539
DOI: https://doi.org/10.1016/j.jpi.2023.100339
Verkko-osoite: https://doi.org/10.1016/j.jpi.2023.100339
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181786525
Detecting cell types from histopathological images is essential for various digital pathology applications. However, large number of cells in whole-slide images (WSIs) necessitates automated analysis pipelines for efficient cell type detection. Herein, we present hematoxylin and eosin (H&E) Image Processing pipeline (HEIP) for automatied analysis of scanned H&E-stained slides. HEIP is a flexible and modular open-source software that performs preprocessing, instance segmentation, and nuclei feature extraction. To evaluate the performance of HEIP, we applied it to extract cell types from ovarian high-grade serous carcinoma (HGSC) patient WSIs. HEIP showed high precision in instance segmentation, particularly for neoplastic and epithelial cells. We also show that there is a significant correlation between genomic ploidy values and morphological features, such as major axis of the nucleus.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
