Brain tumor segmentation using a combination of graph cut and optimized U-Net with genetic algorithm: An efficient...

Accurate segmentation of low-grade gliomas (LGGs) in FLAIR MRI remains challenging due to low-contrast tumor boundaries, irregular morphology, and extreme class imbalance. This work presents a lightweight hybrid segmentation framework that integrates three complementary components: (1) a fast graph-based region refinement step using GrabCut applied during training only to enhance tumor-background contrast, (2) a compact U-Net architecture whose filter configuration is automatically selected using a small-scale genetic algorithm, and (3) an analytical evaluation of lightweight post-processing strategies studied separately from the final GA-optimized model. The method was evaluated on the LGG-MRI-Segmentation dataset using strict patient-level splits of 79 training, 19 validation, and 12 testing patients. The final model achieved strong slice-wise performance with a mean Dice of 0.9132 and a median Dice of 0.9855; the lower mean reflects the influence of more difficult, lower-performing slices and therefore provides a more conservative summary of overall performance across the evaluated test cases. Additional metrics include an IoU of 0.8851, sensitivity of 0.8954, precision of 0.9523, specificity of 0.9997, and an HD95 of 1.00 mm, indicating highly accurate boundary delineation. For contextual benchmarking, a 2D nnU-Net model was trained on the same dataset. nnU-Net achieved a mean validation Dice of 0.8450 and a best validation EMA Dice of 0.9134. Because these nnU-Net Dice values were obtained from its validation workflow rather than the exact slice-wise test procedure used for the proposed model, they are presented as contextual reference results rather than strictly matched test-set estimates. The proposed framework required substantially less training time under the evaluated implementations. Taken together, these findings demonstrate that a carefully coordinated combination of preprocessing, lightweight architectural optimization, and efficient post-processing can achieve strong segmentation performance on the evaluated dataset while remaining computationally efficient. These findings suggest that the proposed framework is promising for computationally constrained LGG segmentation workflows. However, broader clinical applicability requires validation on independent external cohorts before generalizability can be established.