Objective In the clinical diagnosis and grading of brain glioma from histopathological slides, whole-slide cell nucleus density estimation is a critical task. This metric is a key biomarker directly correlated with tumor malignancy, proliferative activity, and patient prognosis, as defined by the World Health Organization (WHO) classification system. Glioma density estimation typically relies heavily on the performance of underlying nucleus segmentation. However, segmentation accuracy is challenged by substantial heterogeneity in nucleus morphology and significant staining variations both across slides and within individual specimens. This variability often causes standard semantic segmentation models to overfit the training data, leading to considerable errors in density estimation. Such inaccuracies can compromise downstream pathological assessments, particularly the subjective and time-consuming manual selection of regions of interest (ROI) for grading. To address these limitations, this study aims to develop a precise and robust whole-slide nucleus density estimation method that enhances model generalization and mitigates overfitting, thereby providing an objective, automated tool for glioma analysis.Methods We propose a systematic three-stage pipeline. (1) Preprocessing: whole-slide images (WSIs) of glioma undergo comprehensive preprocessing, including automated data cleaning to discard blurry or artifact-contaminated patches, data augmentation through geometric transformations (e.g., rotation, flipping) to increase dataset diversity, and color normalization. The latter, based on RGB channel ratios, remaps the color space of all patches to a standardized target, reducing domain shifts caused by staining inconsistencies and improving model robustness. A rigorous semi-automated ground-truth annotation protocol is also implemented, where initial binarization assists annotators in accurately labeling even faint or blurry nuclei, ensuring high-quality training data. (2) Segmentation: using the preprocessed patches, we construct a U-net-based segmentation model that incorporates the DropBlock regularization module—here termed U-net+DropBlock. Unlike standard Dropout, which removes individual neurons, DropBlock eliminates contiguous, spatially correlated regions within feature maps. This structural regularization disrupts undesirable spatial dependencies, forcing the network to learn a more distributed and robust feature representation, thereby reducing overfitting. (3) Quantitative analysis: for each segmented patch, density is computed as the ratio of the total nucleus area to the total patch area—a more robust approach than simple nucleus counting, as it accounts for variations in nucleus size. Patch-wise density values are then assembled into a whole-slide density heatmap, offering an intuitive, global overview of tumor cellularity.Results The U-net+DropBlock model was evaluated both quantitatively and qualitatively against state-of-the-art nucleus segmentation methods, including standard U-net and Hover-net. Quantitatively, our model achieved an F1 score of 90.1%, outperforming U-net and Hover-net, which both scored 87.6%. Qualitative analysis confirmed that our method effectively balances precision and recall, substantially reducing the over-segmentation artifacts common with U-net and the under-segmentation issues observed with Hover-net. This enhanced segmentation quality directly improved the accuracy and reliability of the proposed density estimation approach.Conclusion The proposed whole-slide nucleus density estimation method provides a powerful tool for improving the precision and efficiency of glioma diagnosis. By enabling automated, rapid, and objective analysis of cellular density, it overcomes key limitations of manual pathological review. The generated heatmaps allow pathologists to rapidly identify high-density “hotspots” critical for accurate grading and prognostic evaluation, supporting a more standardized and reproducible ROI selection process. This work lays a solid foundation for developing advanced AI-assisted diagnostic systems, paving the way for more precise, efficient, and reproducible glioma assessments in clinical practice.