  Medical image registration is a fundamental and vital task which will affect
the efficacy of many downstream clinical tasks. Deep learning (DL)-based
deformable image registration (DIR) methods have been investigated, showing
state-of-the-art performance. A test time optimization (TTO) technique was
proposed to further improve the DL models' performance. Despite the substantial
accuracy improvement with this TTO technique, there still remained some regions
that exhibited large registration errors even after many TTO iterations. To
mitigate this challenge, we firstly identified the reason why the TTO technique
was slow, or even failed, to improve those regions' registration results. We
then proposed a two-levels TTO technique, i.e., image-specific optimization
(ISO) and region-specific optimization (RSO), where the region can be
interactively indicated by the clinician during the registration result
reviewing process. For both efficiency and accuracy, we further envisioned a
three-step DL-based image registration workflow. Experimental results showed
that our proposed method outperformed the conventional method qualitatively and
quantitatively.
