用文本提示增强蛇形模型，精准分割医学图像中的复杂器官轮廓。
TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake
- 引入时空演化策略与形态感知机制，提升轮廓建模能力。
- 在脊柱数据集上实现mDice提升6.9%、mBF提升9.1%。
- 适合需要高精度器官分割的医学影像研究者使用。
深蛇（Deep Snake）是一类前景突出的实例分割方法，能精确预测物体轮廓，克服语义分割中常见的掩码空洞和锯齿边缘问题。然而，现有深蛇方法在处理复杂形态变化、捕捉细粒度器官细节以及纠正基础检测错误方面仍存在挑战。为此，我们提出一种新型视觉-语言Mamba蛇形框架TEAMS，包含三项核心创新：(1) 提出时空蛇形演化策略（SSES），通过状态空间模型捕捉蛇形轮廓上的双向空间依赖关系及演化步骤间的时序动态；(2) 设计轮廓形态感知Mamba（CMAM），量化局部轮廓形态以调节Mamba2 SSD双形式中的结构化注意力掩码，增强对输入序列元素相对重要性的感知能力；(3) 构建文本提示协同双头蛇形（TCDHS），融合文本提示信息并将演化轮廓传递至基础检测头，优化深蛇流程并减少误检。在涵盖不同器官与成像模态的五个数据集上的全面评估表明，TEAMS显著优于现有语义与深蛇分割方法（如在脊柱数据集上相对mDice/mBF提升6.9%/9.1%），展现出在多样医学图像分割场景中的可靠潜力。
原文摘要 · Abstract (English)
Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level misclassification issues such as mask cavities and jagged edges in semantic segmentation approaches. However, existing deep snake methods face challenges in handling complex morphological variations, accurately capturing fine-grained organ details, and correcting base detection errors. To mitigate these limitations, we propose a cohesive Text-prompted spatiotEmporal dual-heAd Mamba Snake (TEAMS), a novel vision-language Mamba snake framework with three key innovations: (1) A Spatiotemporal Snake Evolution Strategy (SSES) is introduced to tackle complex morphological variations by capturing bidirectional spatial dependencies along the snake contour and temporal dynamics across evolution steps in a state space model. (2) A Contour Morphology-Aware Mamba (CMAM) is proposed to quantify local contour morphologies to modulate the structured attention mask in the Mamba2 SSD dual form, which extends Mamba's capability to perceive the relative importance of its input sequence elements for better delineation of fine-grained organ details. (3) A Text-prompted Collaborative Dual-Head Snake (TCDHS) is designed to incorporate cues from textual prompts and transfer the evolved contour information to the base detection head, which enhances the deep snake workflow and mitigates wrong detections. Comprehensive evaluations on five datasets covering different organs and imaging modalities demonstrate that TEAMS outperforms existing semantic and deep snake segmentation methods (e.g., relative mDice/mBF improvements of 6.9%/9.1% in a spinal dataset), underscoring its potential as a reliable tool across diverse medical image segmentation scenarios.
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