Ensemble-Based Refinement of Landmark Annotations for DNA Ploidy Analysis in Digital Pathology

Reliable evaluation of image segmentation algorithms in digital pathology depends on high-quality annotation datasets. Landmark-type annotations, essential for cell-counting analyses, are often limited in quality or quantity for segmentation benchmarking, particularly in rare assays where annotation is scarce and costly. In this study, we investigate whether ensemble-inspired refinement of landmark annotations can improve the robustness of segmentation evaluation. Using 15 fluorescently imaged blood samples with more than 20,000 manually placed annotations, we compared three segmentation algorithms—a threshold-based method with clump splitting, a difference-of-Gaussians (DoG) approach, and a convolutional neural network (StarDist)—and used their combined outputs to generate an ensemble-derived ground truth. Confusion matrices and standard metrics (F1 score, precision, and sensitivity) were computed against both manual and ensemble-derived ground truths. Statistical comparisons showed that ensemble-refined annotations reduced noise and decreased mean offsets between annotations and detected objects, yielding more stable evaluation metrics. Our results demonstrate that ensemble-based ground truth generation can guide targeted revision of manual markers, provide a quality measure for annotation reliability, and generate new annotations where no human-generated landmarks exist. This methodology offers a generalizable strategy to strengthen annotation datasets in image cytometry, enabling robust algorithm evaluation in DNA ploidy analysis and potentially in other low-frequency assays.