TSF U-Net: A Texture-Span and Frequency-Gated U-Net for Brain Tumor MRI Segmentation

Abstract

Accurate brain tumor magnetic resonance imaging (MRI) segmentation is essential for objective lesion assessment and treatment planning, yet it remains challenging because tumors exhibit large-scale variations, irregular morphologies, heterogeneous internal textures, and indistinct boundaries. Conventional U-Net variants commonly improve either spatial feature extraction or skip connection fusion, but they do not jointly address the adaptive representation of pathological texture changes and the semantic filtering of noisy shallow features. To bridge this gap, a texture-span and frequency-gated U-Net (TSF U-Net) is proposed, establishing a collaborative spatial and frequency-domain feature-learning framework. Within this framework, a texture-span gated multi-scale (TSGM) block is developed by integrating parallel multi-scale context extraction with a pathological texture-span gating mechanism derived from local maximum and minimum feature responses. This design enables the encoder to emphasize heterogeneous tumor regions and boundary-related variations without requiring additional boundary supervision. Moreover, a discrete cosine frequency-gated fusion (DCT-FGB) block is introduced into the skip connections. It employs deep semantic features to generate top-down frequency-domain gating masks for shallow skip features, allowing useful structural information to be retained while reducing background and noise interference before decoder fusion. On the Brain Tumor Dataset, TSF U-Net achieves an mIoU of 86.471%, a Recall of 87.424%, an F1-score of 84.693%, and a Dice coefficient of 0.819, improving the U-Net baseline by 1.243 percentage points in mIoU and 1.645 percentage points in F1-score. It also achieves the highest mIoU of 88.882% on the BRICS 2025 Dataset. Ablation results confirm the complementary contributions of TSGM and DCT-FGB. The bidirectional cross-dataset experiments yield mIoU values of 77.369% and 70.881%, providing initial evidence of transferability under dataset distribution shifts. These findings indicate that jointly modeling pathological texture variations in the spatial domain and semantically selecting skip features in the frequency domain provides an effective strategy for improving boundary sensitivity, noise resistance, and cross-dataset robustness in brain tumor MRI segmentation.