Abstract
We introduce MARL-MambaContour, the first contour-based medical image segmentation framework based on Multi-Agent Reinforcement Learning (MARL). Our approach reframes segmentation as a multi-agent cooperation task focused on generate topologically consistent object-level contours, addressing the limitations of traditional pixel-based methods which could lack topological constraints and holistic structural awareness of anatomical regions. Each contour point is modeled as an autonomous agent that iteratively adjusts its position to align precisely with the target boundary, enabling adaptation to blurred edges and intricate morphologies common in medical images. This iterative adjustment process is optimized by a contour-specific Soft Actor-Critic (SAC) algorithm, further enhanced with the Entropy Regularization Adjustment Mechanism (ERAM) which dynamically balance agent exploration with contour smoothness. Furthermore, the framework incorporates a Mamba-based policy network featuring a novel Bidirectional Cross-attention Hidden-state Fusion Mechanism (BCHFM). This mechanism mitigates potential memory confusion limitations associated with long-range modeling in state space models, thereby facilitating more accurate inter-agent information exchange and informed decision-making. Extensive experiments on five diverse medical imaging datasets demonstrate the state-of-the-art performance of MARL-MambaContour, highlighting its potential as an accurate and robust clinical application.
Abstract (translated)
我们介绍了MARL-MambaContour，这是首个基于多智能体强化学习（MARL）的轮廓导向医学图像分割框架。我们的方法将分割任务重新定义为一个多代理合作问题，专注于生成具有拓扑一致性的对象级轮廓，以此来解决传统像素基础方法缺乏拓扑约束和整体结构感知的问题，这些问题在解剖区域中尤为明显。每个轮廓点都被建模成一个自治的智能体，它们会迭代地调整自己的位置以精确对齐目标边界，从而能够适应医学图像中常见的模糊边缘和复杂形态。 这一迭代调整过程通过特定于轮廓的软行为者-评论家（SAC）算法进行优化，并进一步利用熵调节机制（ERAM），该机制动态平衡了智能体探索与轮廓平滑度之间的关系。此外，框架还集成了一个基于蟒蛇策略网络，其中包括一种新颖的双向交叉注意力隐藏状态融合机制（BCHFM）。这一机制减轻了长距离建模在状态空间模型中可能产生的记忆混淆限制，从而促进更准确的代理间信息交换和知情决策制定。 广泛的实验表明，在五个不同的医学成像数据集上，MARL-MambaContour展现了顶尖水平的表现，突显了其作为精确且稳健临床应用的巨大潜力。
URL
https://arxiv.org/abs/2506.18679
