Deep learning-based deformable registration methods have been widely
investigated in diverse medical applications. Learning-based deformable
registration relies on weighted objective functions trading off registration
accuracy and smoothness of the deformation field. Therefore, they inevitably
require tuning the hyperparameter for optimal registration performance. Tuning
the hyperparameters is highly computationally expensive and introduces
undesired dependencies on domain knowledge. In this study, we construct a
registration model based on the gradient surgery mechanism, named GSMorph, to
achieve a hyperparameter-free balance on multiple losses. In GSMorph, we
reformulate the optimization procedure by projecting the gradient of similarity
loss orthogonally to the plane associated with the smoothness constraint,
rather than additionally introducing a hyperparameter to balance these two
competing terms. Furthermore, our method is model-agnostic and can be merged
into any deep registration network without introducing extra parameters or
slowing down inference. In this study, We compared our method with
state-of-the-art (SOTA) deformable registration approaches over two publicly
available cardiac MRI datasets. GSMorph proves superior to five SOTA
learning-based registration models and two conventional registration
techniques, SyN and Demons, on both registration accuracy and smoothness.

本文提出一种基于梯度手术机制的注册模型 GSMorph，实现多重损失的超参数自由平衡，在两种公开的心脏 MRI 数据集上实现了优于五种 SOTA 学习型注册模型和两种传统注册技术 SyN 和 Demons 的注册精度和平滑度。