The quantification of the Ki-67 labeling index (LI) is critical for assessing tumor proliferation and prognosis in tumors, yet manual scoring remains a common practice. This study presents an automated workflow for Ki-67 scoring in whole slide images (WSIs) using an Apache Groovy code script for QuPath, complemented by a Python-based post-processing script, providing cell density maps and summary tables. The tissue and cell segmentation are performed using StarDist, a deep learning model, and adaptive thresholding to classify Ki-67 positive and negative nuclei. The pipeline was applied to a cohort of 632 pediatric brain tumor cases with 734 Ki-67-stained WSIs from the Children’s Brain Tumor Network. Medulloblastoma showed the highest Ki-67 LI(median: 19.84), followed by atypical teratoid rhabdoid tumor (median: 19.36). Moderate values were observed in brainstem glioma-diffuse intrinsic pontine glioma (median: 11.50), high-grade glioma (grades 3 & 4) (median: 9.50), and ependymoma (median: 5.88). Lower indices were foundin meningioma (median: 1.84), while the lowest were seen in low-grade glioma (grades 1 & 2)(median: 0.85), dysembryoplastic neuroepithelial tumor (median: 0.63), and ganglioglioma (median:0.50). The results aligned with the consensus of the oncology, demonstrating a significant correlationin Ki-67 LI across most of the tumor families/types, with high malignancy tumors showing thehighest proliferation indices and lower malignancy tumors exhibiting lower Ki-67 LI. The automated approach facilitates the assessment of large amounts of Ki-67 WSIs in research settings.