H&E image analysis pipeline for quantifying morphological features

• Main Authors: Valeria Ariotta (Author), Oskari Lehtonen (Author), Shams Salloum (Author), Giulia Micoli (Author), Kari Lavikka (Author), Ville Rantanen (Author), Johanna Hynninen (Author), Anni Virtanen (Author), Sampsa Hautaniemi (Author)
• Format: Book
• Published: Elsevier, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

2153-3539
10.1016/j.jpi.2023.100339