Brain tumor segmentation remains challenging because the three standard sub-regions, i.e., whole tumor (WT), tumor core (TC), and enhancing tumor (ET), often exhibit ambiguous visual boundaries. Integrating radiological description texts with imaging has shown promise. However, most multimodal approaches typically compress a report into a single global text embedding shared across all sub-regions, overlooking their distinct clinical characteristics. We propose TextCSP (text-modulated soft cascade architecture), a hierarchical text-guided framework that builds on the TextBraTS baseline with three novel components: (1) a text-modulated soft cascade decoder that predicts WT->TC->ET in a coarse-to-fine manner consistent with their anatomical containment hierarchy. (2) sub-region-aware prompt tuning, which uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate specialized text representations tailored for each sub-region; (3) text-semantic channel modulators that convert the aforementioned representations into channel-wise refinement signals, enabling the decoder to emphasize features aligned with clinically described patterns. Experiments on the TextBraTS dataset demonstrate consistent improvements across all sub-regions against state-of-the-art methods by 1.7% and 6% on the main metrics Dice and HD95.

翻译：脑肿瘤分割仍然具有挑战性，因为三种标准子区域——即全肿瘤区域(WT)、肿瘤核心区域(TC)和增强肿瘤区域(ET)——通常呈现模糊的视觉边界。将放射学描述文本与影像相结合已展现出前景。然而，大多数多模态方法通常将报告压缩为所有子区域共享的单一全局文本嵌入，忽略了它们不同的临床特征。我们提出TextCSP（文本调制软级联架构），这是一种层级化文本引导框架，基于TextBraTS基线构建，包含三个创新组件：（1）文本调制软级化解码器，按照WT→TC→ET的顺序以粗到精的方式预测，与其解剖包含层级一致；（2）子区域感知提示调优，使用可学习软提示与LoRA适配的BioBERT编码器，为每个子区域生成专用文本表示；（3）文本语义通道调制器，将上述表示转换为通道级细化信号，使解码器能够强调与临床描述模式对齐的特征。在TextBraTS数据集上的实验表明，与最先进方法相比，所有子区域在主指标Dice和HD95上分别获得1.7%和6%的持续提升。