# Primitive Simultaneous Optimization of Similarity Metrics for Image Registration

Source: [Primitive Simultaneous Optimization of Similarity Metrics for Image Registration](<https://www.sota2.com/research/paper/68fa70d91e209131cea4dab4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:51:52.116Z

Authors: Diana Waldmannstetter, Benedikt Wiestler, Julian Schwarting, Ivan Ezhov, Marie Metz, Spyridon Bakas, Bhakti Baheti, Satrajit Chakrabarty, Daniel Rueckert, Jan S. Kirschke, Rolf A. Heckemann, Marie Piraud, Bjoern H. Menze, Florian Kofler

Published: 2023-04-04T07:43:56.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.01601>)

[PDF](<https://arxiv.org/pdf/2304.01601.pdf>)