TotalRegistrator：面向CT图像配准的轻量级基础模型
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
AI总结：
提出TotalRegistrator，一种基于UNet和场分解的轻量级CT图像配准基础模型，可同时对齐多器官，在内部及外部数据集上表现优异，泛化能力强。
摘要：
图像配准是临床实践中纵向和多期CT图像分析中的一项基本技术。然而，现有的大多数方法都是针对单器官应用量身定制的，限制了它们对其他解剖区域的泛化能力。本工作提出了TotalRegistrator，一种能够使用标准UNet架构和新颖的场分解策略同时对齐多个解剖区域的图像配准框架。该模型是轻量级的，训练仅需11GB的GPU内存。为了训练和评估我们的方法，我们构建了一个大规模纵向数据集，包含来自个体患者在不同时间点获取的695对全身（胸-腹-盆腔）CT扫描。我们将TotalRegistrator与通用的经典迭代算法和最近用于图像配准的基础模型进行了基准测试。为了进一步评估鲁棒性和泛化能力，我们在三个外部数据集上评估了我们的模型：来自Learn2Reg挑战的公共胸部和腹部数据集，以及来自合作医院的私有多期腹部数据集。在内部数据集上的实验结果表明，所提出的方法在多器官腹部配准中通常优于基线方法，但在肺对齐性能上略有下降。在分布外数据集上，尽管未针对这些任务进行微调，它仍取得了与领先的单器官模型相比有竞争力的结果，展示了强大的泛化能力。源代码将在https://github.com/DIAGNijmegen/oncology_image_registration.git公开提供。
英文摘要：
Image registration is a fundamental technique in the analysis of longitudinal and multi-phase CT images within clinical practice. However, most existing methods are tailored for single-organ applications, limiting their generalizability to other anatomical regions. This work presents TotalRegistrator, an image registration framework capable of aligning multiple anatomical regions simultaneously using a standard UNet architecture and a novel field decomposition strategy. The model is lightweight, requiring only 11GB of GPU memory for training. To train and evaluate our method, we constructed a large-scale longitudinal dataset comprising 695 whole-body (thorax-abdomen-pelvic) paired CT scans from individual patients acquired at different time points. We benchmarked TotalRegistrator against a generic classical iterative algorithm and a recent foundation model for image registration. To further assess robustness and generalizability, we evaluated our model on three external datasets: the public thoracic and abdominal datasets from the Learn2Reg challenge, and a private multiphase abdominal dataset from a collaborating hospital. Experimental results on the in-house dataset show that the proposed approach generally surpasses baseline methods in multi-organ abdominal registration, with a slight drop in lung alignment performance. On out-of-distribution datasets, it achieved competitive results compared to leading single-organ models, despite not being fine-tuned for those tasks, demonstrating strong generalizability. The source code will be publicly available at: https://github.com/DIAGNijmegen/oncology_image_registration.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
