An Efficient and Automated Brain Tumor Segmentation in MRI Images Using Non-Supervised Model

Abstract

Brain neoplasm is life-threatening abnormalities that require accurate segmentation for effective diagnosis along with treatment planning. Existing segmentation methods often depend on manual annotations or supervised learning, which are time-consuming, subjective, and not scalable. To overcome these challenges, this study proposes a fully unsupervised brain tumor segmentation framework using multimodal MRI data. The 3D MRI volumes are first sliced into 2D axial views and preprocessed to enhance quality. K-means clustering is then applied to generate pseudo-labels that distinguish tumor from healthy regions. These labels guide M-Net CNN training process, which produces coarse tumor localization. A U-Net model further refines the segmentation by fusing the original sliced MRI inputs with M-Net CNN outputs to enhance boundary precision and anatomical alignment. The proposed pipeline achieved a high Dice coefficient of 84.2%, demonstrating its accuracy and robustness. This multi-stage, annotation-free approach enables precise tumor delineation and is suitable for large-scale clinical integration.