3D brain tumor segmentation in MRI images using hierarchical adaptive pruning of non-tumor regions

Abstract

Background: The detection of brain tumors in MRI images has significantly improved with the advent of deep learning methods. However, these approaches often suffer from high complexity, computational cost, and the need for extensive annotated training data, making them less practical for real-time and patient-centered diagnostic systems. To address these challenges, this study introduces a perceptually inspired, algorithmic method that mimics the diagnostic behavior of physicians, offering a lightweight and interpretable alternative for brain tumor segmentation. Method: We propose a novel adaptive hierarchical pruning algorithm for 3D MRI brain images that iteratively removes low-intensity, non-tumor voxels based on the statistical distribution of intensities. The tumor region is identified through the comparison of the remaining pixel intensity values statistics. The pruning automatically stops when the mean and median of the remaining voxels converge, leaving the candidate tumor region. Results: The proposed algorithm was evaluated on all patients of the BraTS2019 and BraTS2023 datasets, achieving segmentation accuracies of 99.1 % and 99.13 %, respectively. It demonstrated high sensitivity and specificity compared to several deep learning methods, showing robust performance across diverse patient scans. Conclusions: This study demonstrates that a simple, perceptually driven segmentation algorithm can match or outperform complex deep learning models, particularly in clinical settings where lightweight, transparent, and efficient tools are essential.