Region Specific Optimization (RSO)-based Deep Interactive Registration
This work addresses a domain-specific challenge in medical imaging by enabling more precise deformable image registration, which is incremental as it builds on existing test time optimization techniques.
The paper tackled the problem of persistent registration errors in deep learning-based medical image registration by proposing a two-level test time optimization technique (ISO and RSO) that allows clinicians to interactively indicate problematic regions, resulting in improved accuracy and efficiency over conventional methods.
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.