TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration

• URL: http://arxiv.org/abs/2508.04450v1
• Date: Wed, 06 Aug 2025 13:50:27 GMT
• Title: TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
• Authors: Xuan Loc Pham, Gwendolyn Vuurberg, Marjan Doppen, Joey Roosen, Tip Stille, Thi Quynh Ha, Thuy Duong Quach, Quoc Vu Dang, Manh Ha Luu, Ewoud J. Smit, Hong Son Mai, Mattias Heinrich, Bram van Ginneken, Mathias Prokop, Alessa Hering,
• Abstract summary: TotalRegistrator is an image registration framework capable of aligning multiple anatomical regions simultaneously.<n>The model is lightweight, requiring only 11GB of GPU memory for training.
• Score: 2.7337927055013815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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