Data-Driven Registration and Modeling of Brain Deformation for Image-Guided Neurosurgery: A Systematic Review
Organizations: LASIGE, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal · Department of Neurosurgery and Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA · Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 2W1, Canada · Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK · Department of Oncology, University of Cambridge, Cambridge, CB2 0AH, UK · Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK · Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA · Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
Abstract
Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induce tissue motion, causing preoperative planning images to become misaligned with the intraoperative anatomy. In this systematic review, we examine data-driven methods developed between 2020 and 2025 for brain deformation registration and modeling, with a particular focus on learning-based approaches. A comprehensive literature search was conducted in PubMed, IEEE Xplore, Scopus, and Web of Science using predefined inclusion and exclusion criteria for computational methods addressing brain deformation in neurosurgical imaging, resulting in 46 eligible studies. We provide a unified analysis of methodological strategies, including deep learning-based image registration, direct deformation field regression, synthesis-driven multimodal alignment, resection-aware architectures for handling missing correspondences, and hybrid models integrating biomechanical priors. We also examine dataset utilization, evaluation metrics, validation protocols, and the assessment of uncertainty and generalization across studies. While learning-based methods demonstrate promising accuracy and computational efficiency, current approaches remain limited by out-of-distribution robustness, standardized benchmarking, interpretability, and readiness for clinical deployment. Our review highlights these gaps and outlines future directions toward more robust, generalizable, and clinically translatable solutions for neurosurgical guidance. By organizing recent advances and critically assessing evaluation practices, this work provides a comprehensive reference for researchers and clinicians working on data-driven registration and modeling of brain deformation.