Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
This addresses a gap in image registration for medical imaging, specifically for glioma MRI analysis, but is incremental as it adapts an established method from semantic segmentation.
The paper tackled the lack of simultaneous optimization of similarity metrics in image registration by testing primitive summation in a multi-modal 3D setting, resulting in improved registration accuracy measured by TRE on expert-annotated glioma MRI datasets.
Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration. To help closing this gap in the literature, we investigate in a complex multi-modal 3D setting whether simultaneous optimization of registration metrics, here implemented by means of primitive summation, can benefit image registration. We evaluate two challenging datasets containing collections of pre- to post-operative and pre- to intra-operative MR images of glioma. Employing the proposed optimization, we demonstrate improved registration accuracy in terms of TRE on expert neuroradiologists' landmark annotations.