A Systematic Review on Data-Driven Brain Deformation Modeling for Image-Guided Neurosurgery

2026-02-09

AI Summary

Intraoperative brain tissue deformation causes misalignment between preoperative imaging and actual anatomy, severely compromising the accuracy of neurosurgical navigation. This study systematically reviews 41 AI-driven brain deformation modeling approaches published between 2020 and 2025, encompassing deep learning–based registration, deformation field regression, multimodal synthetic alignment, resection-aware modeling, and biomechanically informed hybrid methods. Evaluated under a unified assessment framework for the first time, these methods reveal critical bottlenecks in out-of-distribution robustness, interpretability, and clinical deployment readiness. The review further identifies the absence of standardized evaluation protocols and insufficient generalization capability as core challenges, and proposes future research directions explicitly oriented toward clinical translation.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Adversarial Attacks & Robustness
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate compensation of brain deformation is a critical challenge for reliable image-guided neurosurgery, as surgical manipulation and tumor resection induce tissue motion that misaligns preoperative planning images with intraoperative anatomy and longitudinal studies. In this systematic review, we synthesize recent AI-driven approaches developed between January 2020 and April 2025 for modeling and correcting brain deformation. A comprehensive literature search was conducted in PubMed, IEEE Xplore, Scopus, and Web of Science, with predefined inclusion and exclusion criteria focused on computational methods applied to brain deformation compensation for neurosurgical imaging, resulting in 41 studies meeting these criteria. 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 addressing missing correspondences, and hybrid models that integrate biomechanical priors. We also examine dataset utilization, reported evaluation metrics, validation protocols, and how uncertainty and generalization have been assessed across studies. While AI-based deformation models demonstrate promising performance and computational efficiency, current approaches exhibit limitations in out-of-distribution robustness, standardized benchmarking, interpretability, and readiness for clinical deployment. Our review highlights these gaps and outlines opportunities for future research aimed at achieving more robust, generalizable, and clinically translatable deformation compensation solutions for neurosurgical guidance. By organizing recent advances and critically evaluating evaluation practices, this work provides a comprehensive foundation for researchers and clinicians engaged in developing and applying AI-based brain deformation methods.

Problem

• brain deformation
• image-guided neurosurgery
• intraoperative misalignment
• surgical navigation
• tissue motion

Innovation

• brain deformation modeling
• deep learning
• image-guided neurosurgery
• resection-aware architectures
• biomechanical priors