{"ID":2885796,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04450","arxiv_id":"2508.04450","title":"TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration","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.","short_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 registra...","url_abs":"https://arxiv.org/abs/2508.04450","url_pdf":"https://arxiv.org/pdf/2508.04450v1","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\"]","published":"2025-08-06T13:50:27Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":611241,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2885796,"paper_url":"https://arxiv.org/abs/2508.04450","paper_title":"TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration","repo_url":"https://github.com/DIAGNijmegen/oncology_image_registration.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}