The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge

TL;DR

The ReMIND2Reg 2025 Challenge introduces a large benchmark dataset for evaluating algorithms that align intraoperative ultrasound with preoperative MRI in brain tumor surgery, aiming to improve intraoperative guidance accuracy.

Contribution

It provides the first large-scale, standardized benchmark dataset and evaluation framework for multimodal brain image registration during neurosurgery.

Findings

Largest public dataset for brain image registration

Benchmarking of registration algorithms using clinical metrics

Encourages development of robust, generalizable methods

Abstract

Accurate intraoperative image guidance is critical for achieving maximal safe resection in brain tumor surgery, yet neuronavigation systems based on preoperative MRI lose accuracy during the procedure due to brain shift. Aligning post-resection intraoperative ultrasound (iUS) with preoperative MRI can restore spatial accuracy by estimating brain shift deformations, but it remains a challenging problem given the large anatomical and topological changes and substantial modality intensity gap. The ReMIND2Reg 2025 Challenge provides the largest public benchmark for this task, built upon the ReMIND dataset. It offers 99 training cases, 5 validation cases, and 10 private test cases comprising paired 3D ceT1 MRI, T2 MRI, and post-resection 3D iUS volumes. Data are provided without annotations for training, while validation and test performance are evaluated on manually annotated anatomical landmarks. Metrics include target registration error (TRE), robustness to worst-case landmark misalignment (TRE30), and runtime. By establishing a standardized evaluation framework for this clinically critical and technically complex problem, ReMIND2Reg aims to accelerate the development of robust, generalizable, and clinically deployable multimodal registration algorithms for image-guided neurosurgery.