通过深度平衡模型弥合经典与基于学习的迭代配准
Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Models
AI总结：
提出DEQReg，基于深度平衡模型将配准视为平衡求解问题，实现恒定内存与稳定收敛，在脑MRI和肺CT数据集上达到竞争性能并显著降低内存消耗。
摘要：
可变形医学图像配准传统上被表述为一个优化问题。经典方法迭代地解决这一问题，而近期的基于学习方法则使用循环神经网络（RNNs）通过以固定步数展开变形场预测来模拟这一过程。然而，经典方法通常在足够迭代后收敛，但基于学习的展开方法缺乏理论收敛保证，并在经验上表现出不稳定性。此外，展开方法在训练时存在实际瓶颈：由于通过时间的反向传播（BPTT），GPU内存使用量随展开步数线性增长。为解决理论和实践两方面的挑战，我们提出了DEQReg，一种基于深度平衡模型（DEQ）的新型配准框架，该框架将配准表述为寻求平衡的问题，在经典优化与基于学习的展开方法之间建立了自然联系。DEQReg保持恒定的内存使用，支持理论上无限的迭代步数。通过在公共脑部MRI和肺部CT数据集上的广泛评估，我们表明DEQReg能够实现具有竞争力的配准性能，同时与最先进的展开方法相比大幅减少内存消耗。我们还揭示了一个有趣的现象：当推理步数超过训练配置时，现有展开方法的性能先略有提升，随后不可逆地下降。相比之下，DEQReg凭借其内置的寻求平衡机制实现稳定收敛，弥合了基于经典优化与基于现代学习的配准方法之间的差距。
英文摘要：
Deformable medical image registration is traditionally formulated as an optimization problem. While classical methods solve this problem iteratively, recent learning-based approaches use recurrent neural networks (RNNs) to mimic this process by unrolling the prediction of deformation fields in a fixed number of steps. However, classical methods typically converge after sufficient iterations, but learning-based unrolling methods lack a theoretical convergence guarantee and show instability empirically. In addition, unrolling methods have a practical bottleneck at training time: GPU memory usage grows linearly with the unrolling steps due to backpropagation through time (BPTT). To address both theoretical and practical challenges, we propose DEQReg, a novel registration framework based on Deep Equilibrium Models (DEQ), which formulates registration as an equilibrium-seeking problem, establishing a natural connection between classical optimization and learning-based unrolling methods. DEQReg maintains constant memory usage, enabling theoretically unlimited iteration steps. Through extensive evaluation on the public brain MRI and lung CT datasets, we show that DEQReg can achieve competitive registration performance, while substantially reducing memory consumption compared to state-of-the-art unrolling methods. We also reveal an intriguing phenomenon: the performance of existing unrolling methods first increases slightly then degrades irreversibly when the inference steps go beyond the training configuration. In contrast, DEQReg achieves stable convergence with its inbuilt equilibrium-seeking mechanism, bridging the gap between classical optimization-based and modern learning-based registration methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
