Application of StarDist to Diagnostic-Grade White Blood Cells Segmentation in Whole Slide Images

Abstract

Accurate and automated segmentation of white blood cells (WBCs) in whole slide images (WSIs) is a critical step in computational pathology. This study presents a comprehensive evaluation and enhancement of the StarDist algorithm, leveraging its star-convex polygonal modeling to improve segmentation precision in complex WSI datasets. Our pipeline integrates tailored preprocessing, expert annotations from QuPath, and adaptive learning strategies for model training. Comparative analysis with U-Net and Mask R-CNN demonstrates StarDist’s superiority across multiple performance metrics, including Dice coefficient (0.89), precision (0.99), and IoU (0.95). Visual evaluations further highlight its robustness in handling overlapping cells and staining inconsistencies. The study establishes StarDist as a reliable tool for digital pathology, with potential integration into clinical decision-support systems. In addition to Dice and IoU, metrics such as Aggregated Jaccard Index and Boundary F1-Score are gaining popularity for biomedical segmentation. Preprocessing techniques like Macenko stain normalization and adaptive histogram equalization can further improve generalizability. QuPath, an open-source digital pathology platform, was utilized to perform accurate WBC annotations prior to training and evaluation.