Melanoma brain metastases (MBM) are common and spatially heterogeneous lesions, complicating cohort-level analyses due to anatomical variability and differing MRI protocols. We propose a fully differentiable, deep-learning-based deformable registration framework that aligns individual pathological brains to a common atlas while preserving metastatic tissue without requiring lesion masks or preprocessing. Missing anatomical correspondences caused by metastases are handled through a forward-model similarity metric based on distance-transformed anatomical labels, combined with a volume-preserving regularization term to ensure deformation plausibility. Registration performance was evaluated using Dice coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), and Jacobian-based measures. The method was applied to 209 MBM patients from three centres, enabling standardized mapping of metastases to anatomical, arterial, and perfusion atlases. The framework achieved high registration accuracy across datasets (DSC 0.89-0.92, HD 6.79-7.60 mm, ASSD 0.63-0.77 mm) while preserving metastatic volumes. Spatial analysis demonstrated significant over-representation of MBM in the cerebral cortex and putamen, under-representation in white matter, and consistent localization near the gray-white matter junction. No arterial territory showed increased metastasis frequency after volume correction. This approach enables robust atlas registration of pathological brain MRI without lesion masks and supports reproducible multi-centre analyses. Applied to MBM, it confirms and refines known spatial predilections, particularly preferential seeding near the gray-white matter junction and cortical regions. The publicly available implementation facilitates reproducible research and extension to other brain tumours and neurological pathologies.

翻译：黑色素瘤脑转移（MBM）是常见且空间异质性的病灶，由于解剖结构变异和不同MRI协议的存在，使得队列水平分析变得复杂。我们提出了一种完全可微分的、基于深度学习的可变形配准框架，该框架将个体病理脑与公共图谱对齐，同时保留转移组织，且无需病灶掩膜或预处理。由转移灶引起的解剖对应缺失通过基于距离变换解剖标签的前向模型相似性度量来处理，并结合体积保持正则化项以确保形变的合理性。配准性能通过Dice系数（DSC）、Hausdorff距离（HD）、平均对称表面距离（ASSD）以及基于雅可比行列式的度量进行评估。该方法应用于来自三个中心的209名MBM患者，实现了将转移灶标准化映射到解剖、动脉和灌注图谱。该框架在不同数据集中实现了高配准精度（DSC 0.89-0.92，HD 6.79-7.60 mm，ASSD 0.63-0.77 mm），同时保留了转移体积。空间分析显示MBM在大脑皮层和壳核中显著过表达，在白质中表达不足，并一致定位于灰白质交界附近。经体积校正后，未发现任何动脉区域显示转移频率增加。该方法无需病灶掩膜即可实现病理脑MRI的稳健图谱配准，并支持可重复的多中心分析。应用于MBM时，它证实并完善了已知的空间偏好，特别是灰白质交界和皮层区域附近的优先定植。公开可用的实现促进了可重复性研究，并可扩展到其他脑肿瘤和神经系统病理。