RT Journal Article
T1 Cell Detection in Biomedical Immunohistochemical Images Using Unsupervised Segmentation and Deep Learning
A1 Al Tarawneh, Zakaria A.
A1 Tarawneh, Ahmad S.
A1 Mbaidin, Almoutaz
A1 Fernández Delgado, Manuel
A1 Gándara Vila, Pilar
A1 Hassanat, Ahmad
A1 Cernadas García, Eva
K1 Cell detection
K1 Immunohistochemical images
K1 Image segmentation
K1 Medical image segmentation
K1 Oral cancer
K1 Deep learning
K1 YOLO
K1 U-Net
AB Accurate computer-aided cell detection in immunohistochemistry images of different tissues is essential for advancing digital pathology and enabling large-scale quantitative analysis. This paper presents a comprehensive comparison of six unsupervised segmentation methods against two supervised deep learning approaches for cell detection in immunohistochemistry images. The unsupervised methods are based on the continuity and similarity image properties, using techniques like clustering, active contours, graph cuts, superpixels, or edge detectors. The supervised techniques include the YOLO deep learning neural network and the U-Net architecture with heatmap-based localization for precise cell detection. All these methods were evaluated using leave-one-image-out cross-validation on the publicly available OIADB dataset, containing 40 oral tissue IHC images with over 40,000 manually annotated cells, assessed using precision, recall, and 𝐹1-score metrics. The U-Net model achieved the highest performance for cell nuclei detection, an 𝐹1-score of 75.3%, followed by YOLO with 𝐹1 = 74.0%, while the unsupervised OralImmunoAnalyser algorithm achieved only 𝐹1 = 46.4%. Although the two former are the best solutions for automatic pathological assessment in clinical environments, the latter could be useful for small research units without big computational resources.
PB MDPI
YR 2025
FD 2025
LK https://hdl.handle.net/10347/46988
UL https://hdl.handle.net/10347/46988
LA eng
NO Al-Tarawneh, Z. A., Tarawneh, A. S., Mbaidin, A., Fernández-Delgado, M., Gándara-Vila, P., Hassanat, A., & Cernadas, E. (2025). Cell Detection in Biomedical Immunohistochemical Images Using Unsupervised Segmentation and Deep Learning. Electronics, 14(18), 3705. https://doi.org/10.3390/electronics14183705
NO This work has received financial support from the Xunta de Galicia—Consellería de Cultura, Educación, Formación Profesional e Universidades (Centro de investigación de Galicia accreditation 2024–2027, ED431G-2023/04) and the European Union (European Regional Development Fund—ERDF).
DS Minerva
RD 21 may 2026