Even though simultaneous optimization of similarity metrics represents a standard procedure in the field of semantic segmentation, surprisingly, this does not hold true for image registration. To close this unexpected 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 Imaging (MRI) of glioma. Employing the proposed optimization we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) on expert neuroradiologists' landmark annotations.

翻译：尽管相似性度量的同步优化在语义分割领域是一种标准流程，但令人惊讶的是，这在图像配准中并不成立。为填补文献中的这一意外空白，我们在复杂的多模态3D场景中研究了配准度量的同步优化——此处通过原始求和方式实现——是否有利于图像配准。我们评估了两个具有挑战性的数据集，其中包括胶质瘤术前至术后及术前至术中的磁共振成像（MRI）集合。通过采用所提出的优化方法，我们证明在专家神经放射学家标注的地标点上，以目标配准误差（TRE）为指标，配准精度得到了提升。