<p>Accurate segmentation of brain tissues and tumors in magnetic resonance (MR) images is vital for diagnosis and treatment planning. Challenges such as noise and intensity inhomogeneity or bias field degrade segmentation accuracy. We propose DF-VPFCwBC (Deep-Feature Vector Picture Fuzzy Clustering with Bias Correction), a hybrid pipeline that first extracts spatial-semantic feature vectors using an Attention U-Net and then performs Vector Picture Fuzzy Clustering with simultaneous bias estimation and correction. The method integrates picture fuzzy membership (positive, neutral, negative, refusal) and a vector bias parameter in a single objective function, enabling robust three-dimensional (3D) segmentation. On synthetic data with sinusoidal bias, DF-VPFCwBC produced a peak signal-to-noise ratio of about 43.20 dB, a structural similarity index close to 0.92, a misclassification error around 0.01, and more than sixty-four thousand correctly classified pixels, which is a substantial improvement over conventional fuzzy clustering method. On the BRATS held-out set, the proposed approach achieved an average Dice score of about 0.91, a Jaccard index near 0.83, and a bias estimation error (RMSE) of roughly 0.04, whereas Attention U-Net alone yielded lower values. Across more than one hundred MR scans, DF-VPFCwBC consistently reduced segmentation errors and delivered volumetric reconstructions with high fidelity while maintaining acceptable computational time. These findings confirm that integrating deep features with vector picture-fuzzy clustering and bias estimation results in robust, accurate, and efficient 3D brain tumor segmentation.</p>