标题： RL-U$^2$网：一种带有强化学习辅助多模态特征融合的双分支UNet用于精确的3D全心分割 显示英文标题

标题： RL-U$^2$Net: A Dual-Branch UNet with Reinforcement Learning-Assisted Multimodal Feature Fusion for Accurate 3D Whole-Heart Segmentation

摘要： 准确的全心分割是心血管疾病精确诊断和干预规划的关键组成部分。 整合来自计算机断层扫描（CT）和磁共振成像（MRI）等模态的互补信息可以显著提高分割的准确性和鲁棒性。 然而，现有的多模态分割方法面临几个限制：模态之间的严重空间不一致阻碍了有效特征融合；融合策略通常是静态的，缺乏适应性；特征对齐和分割的过程是解耦的，效率低下。 为了解决这些挑战，我们提出了一种由强化学习增强的双分支U-Net架构，用于特征对齐，称为RL-U$^2$Net，旨在实现精确且高效的多模态3D全心分割。 该模型采用双分支U型网络并行处理CT和MRI图像块，并在编码器之间引入了一个新颖的RL-XAlign模块。 该模块采用跨模态注意力机制来捕捉模态之间的语义对应关系，同时强化学习代理学习一种最优的旋转策略，以一致地对齐解剖姿态和纹理特征。 然后通过各自的解码器对对齐后的特征进行重建。 最后，一个基于集成学习的决策模块将各个图像块的预测结果进行整合，生成最终的分割结果。 在公开的MM-WHS 2017数据集上的实验结果表明，所提出的RL-U$^2$Net优于现有的最先进方法，在CT上达到了93.1%的Dice系数，在MRI上达到了87.0%，从而验证了所提出方法的有效性和优越性。 显示英文摘要

摘要： Accurate whole-heart segmentation is a critical component in the precise diagnosis and interventional planning of cardiovascular diseases. Integrating complementary information from modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) can significantly enhance segmentation accuracy and robustness. However, existing multi-modal segmentation methods face several limitations: severe spatial inconsistency between modalities hinders effective feature fusion; fusion strategies are often static and lack adaptability; and the processes of feature alignment and segmentation are decoupled and inefficient. To address these challenges, we propose a dual-branch U-Net architecture enhanced by reinforcement learning for feature alignment, termed RL-U$^2$Net, designed for precise and efficient multi-modal 3D whole-heart segmentation. The model employs a dual-branch U-shaped network to process CT and MRI patches in parallel, and introduces a novel RL-XAlign module between the encoders. The module employs a cross-modal attention mechanism to capture semantic correspondences between modalities and a reinforcement-learning agent learns an optimal rotation strategy that consistently aligns anatomical pose and texture features. The aligned features are then reconstructed through their respective decoders. Finally, an ensemble-learning-based decision module integrates the predictions from individual patches to produce the final segmentation result. Experimental results on the publicly available MM-WHS 2017 dataset demonstrate that the proposed RL-U$^2$Net outperforms existing state-of-the-art methods, achieving Dice coefficients of 93.1% on CT and 87.0% on MRI, thereby validating the effectiveness and superiority of the proposed approach.