Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
摘要
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 Magnetic Resonance (MR) images of glioma. Employing the proposed optimization, we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) o on expert neuroradiologists’ landmark annotations.