公开99个病例数据集，提升脑肿瘤手术中影像配准精度。
The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge
- 用术中超声与术前MRI配准，估算脑组织移位变形。
- 99个训练案例，10个私有测试案例，评估配准误差和速度。
- 适合研究医学影像融合、神经外科导航的算法开发者。
术中精准影像引导对实现脑肿瘤安全最大切除至关重要，但基于术前MRI的神经导航系统因脑移位而精度下降。将术后术中超声（iUS）与术前MRI配准可恢复空间准确性，但因解剖结构和拓扑变化大、模态强度差异显著，仍具挑战性。ReMIND2Reg 2025挑战赛提供该任务最大的公开基准，基于ReMIND数据集构建，包含99个训练病例、5个验证病例和10个私有测试病例，涵盖配对的3D ceT1 MRI、T2 MRI和术后3D iUS体积数据。训练数据无标注，验证与测试通过人工标注的解剖标志点评估，指标包括目标配准误差（TRE）、最差情况标志点误配下的鲁棒性（TRE30）及运行时间。该挑战赛建立标准化评估框架，旨在加速开发适用于临床的鲁棒、泛化性强的多模态图像配准算法。
原文摘要 · Abstract (English)
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.
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