Cell Detection in Biomedical Immunohistochemical Images Using Unsupervised Segmentation and Deep Learning

Abstract

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.