TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake

2026-08-18

AI Summary

为解决深蛇方法在处理复杂形态变化、捕捉精细器官细节及纠正基础检测错误上的挑战，提出了一种文本提示时空双头曼巴蛇框架（TEAMS），通过引入时空蛇进化策略、轮廓形态感知曼巴和文本提示协作双头蛇三项创新。

Abstract

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.

Problem

• deep snake
• morphological variations
• fine-grained organ details
• detection errors

Innovation

• Spatiotemporal Snake Evolution Strategy (SSES)
• Contour Morphology-Aware Mamba (CMAM)
• Text-prompted Collaborative Dual-Head Snake (TCDHS)