arXiv 2602.129332026-02-13cs.CVcs.AIphysics.med-ph

深度学习解剖图谱注册用于黑色素瘤脑转移：在保留病理学的同时实现队列级分析

Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses

Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg（放射肿瘤科，弗赖堡大学医学中心-弗赖堡大学医学院）；Division of Medical Physics, Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg（医学物理系，放射肿瘤科，弗赖堡大学医学中心-弗赖堡大学医学院）；Department of Radiation Oncology, University Medical Center Schleswig-Holstein（放射肿瘤科，石勒苏益格-荷尔斯泰因大学医学中心）；Department of Radiation Oncology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich（放射肿瘤科，慕尼黑技术大学医学院及健康科学系，慕尼黑技术大学医院雷chts der Isar）

Nanna E. Wielenberg, Ilinca Popp, Oliver Blanck, Lucas Zander, Jan C. Peeken, Stephanie E. Combs, Anca-Ligia Grosu, Dimos Baltas, Tobias Fechter

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AI总结：

本文提出深度学习方法实现黑色素瘤脑转移的精准图谱注册，无需病变掩膜即可在多中心数据中实现标准化分析，验证了转移灶的空间偏好。

摘要：

黑色素瘤脑转移（MBM）是常见且空间异质性的病变，由于解剖变异性和不同的MRI协议，使得队列级分析复杂化。我们提出了一种完全可微分的深度学习变形注册框架，该框架将个体病理脑注册到一个通用图谱，同时在不需病变掩膜或预处理的情况下保留转移性组织。由于转移导致的解剖对应关系缺失通过基于距离变换的解剖标签的相似性度量来处理，结合体积保持正则化项以确保变形的合理性。注册性能通过Dice系数（DSC）、Hausdorff距离（HD）、平均对称表面距离（ASSD）以及基于雅可比的度量进行评估。该方法应用于来自三个中心的209例MBM患者，实现了对转移灶到解剖、动脉和灌注图谱的标准化映射。该框架在不同数据集上实现了高注册精度（DSC 0.89-0.92，HD 6.79-7.60 mm，ASSD 0.63-0.77 mm），同时保留了转移性体积。空间分析显示MBM在大脑皮层和尾状核中显著过表达，在白质中显著不足，并且在灰质-白质交界处有持续定位。在体积校正后，没有动脉区域显示出转移频率的增加。该方法实现了在不使用病变掩膜的情况下对病理脑MRI的鲁棒图谱注册，支持可重复的多中心分析。应用于MBM，它确认并细化了已知的空间偏好，特别是转移倾向于灰质-白质交界处和皮层区域。公开可用的实现促进了可重复的研究，并可扩展到其他脑肿瘤和神经病理学。

英文摘要：

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.

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