Image Segmentation Techniques for Brain Tumor Localization

Abstract

Accurate localization of brain tumors from magnetic resonance imaging (MRI) is critical for diagnosis, surgical planning, radiotherapy contouring, and longitudinal monitoring. This manuscript reviews and operationalizes state-of-the-art image segmentation techniques for brain tumor localization, spanning classical image processing pipelines to modern deep neural architectures that fuse convolution and attention. We analyze practical challenges—heterogeneous tumor phenotypes across patients and scanners, small and imbalanced targets (e.g., enhancing tumor), intensity non-standardization, and domain shift—and translate them into design choices for robust systems. Building on these insights, we develop a unified pipeline comprising multi-modal MRI preprocessing (N4 bias correction, skull stripping, z-score standardization, and rigid co-registration), a model zoo of 3D U-Net variants (vanilla, attention, UNet++), and a transformer-augmented architecture (Swin-UNETR).

We combine Dice and boundary-aware losses, strong 3D augmentations, and uncertainty-aware post-processing with connected-component filtering and a 3D CRF. A five-fold cross-validated simulation on multi-modal MRI demonstrates that transformer-augmented and nested skip-connection models improve Dice and Hausdorff distance over a 3D U-Net baseline, with statistically significant gains particularly for enhancing tumor. We further probe robustness to artifacts, label noise, and domain shift via intensity perturbations and style transfer. The results suggest that (i) multi-modal fusion and hierarchical features are indispensable, (ii) small-lesion sensitivity benefits from attention and boundary losses, and (iii) simple two-model ensembles deliver consistent, clinically meaningful improvements while preserving inference efficiency. Limitations and avenues for deployment—calibration, active learning, and test-time adaptation—are discussed.