Ki67 Expression Classification from HE Images with Semi-Automated Computer-Generated Annotations

Dominika Petríková, Ivan Cimrák, Katarína Tobiášová, Lukáš Plank

2024

Abstract

Ki67 protein plays crucial role in cell proliferation and it is considered a good marker for determining the cell growth. In histopathology, it is often assessed by immunohistochemistry (IHC) staining. Even though IHC is considered common practice in clinical diagnosis, it has several limitations such as variability and subjectivity. Meaning interpretation of IHC can be subjective and vary between individuals. Moreover, quantification can be challenging as well as it is cost and time consuming. Therefore neural network models hold promise for improving this area, however they require a large amount of high quality annotated dataset, which is time-consuming and laborious work for experts. In the paper, we employed the proposed semi-automated approach of generating Ki67 score from pairs of hematoxylin and eosin (HE) and IHC slides, which aims to minimize expert assistance. The approach consists of image analysis methods such as clustering optimization for tissue registration. Using a sample of 84 pairs of whole slide images of seminomas tissue stained by HE and IHC, we generated dataset containing approximately 30 thousand labeled patches. On the HE patches annotated by proposed approach, we executed several experiments on fine-tuning neural networks model to predict Ki67 score from HE images.

Paper Citation

in Harvard Style

Petríková D., Cimrák I., Tobiášová K. and Plank L. (2024). Ki67 Expression Classification from HE Images with Semi-Automated Computer-Generated Annotations. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 536-544. DOI: 10.5220/0012535900003657

in Bibtex Style

@conference{bioinformatics24,
author={Dominika Petríková and Ivan Cimrák and Katarína Tobiášová and Lukáš Plank},
title={Ki67 Expression Classification from HE Images with Semi-Automated Computer-Generated Annotations},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={536-544},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012535900003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Ki67 Expression Classification from HE Images with Semi-Automated Computer-Generated Annotations
SN - 978-989-758-688-0
AU - Petríková D.
AU - Cimrák I.
AU - Tobiášová K.
AU - Plank L.
PY - 2024
SP - 536
EP - 544
DO - 10.5220/0012535900003657
PB - SciTePress