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

• URL: http://arxiv.org/abs/2602.10155v1
• Date: Mon, 09 Feb 2026 22:11:56 GMT
• Title: A Systematic Review on Data-Driven Brain Deformation Modeling for Image-Guided Neurosurgery
• Authors: Tiago Assis, Colin P. Galvin, Joshua P. Castillo, Nazim Haouchine, Marta Kersten-Oertel, Zeyu Gao, Mireia Crispin-Ortuzar, Stephen J. Price, Thomas Santarius, Yangming Ou, Sarah Frisken, Nuno C. Garcia, Alexandra J. Golby, Reuben Dorent, Ines P. Machado,
• Abstract summary: Accurate compensation of brain deformation is a critical challenge for reliable image-guided neurosurgery.<n>Recent AI-driven approaches developed between January 2020 and April 2025 for modeling and correcting brain deformation.
• Score: 29.978282112659993
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• AI-Driven Radiology Report Generation for Traumatic Brain Injuries [0.0]
  We propose an AI-based approach for automatic radiology report generation tailored to cranial trauma cases.<n>Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data.<n>We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset.
  arXiv  Detail & Related papers  (2025-10-09T17:39:04Z)
• A Federated Learning Framework for Handling Subtype Confounding and Heterogeneity in Large-Scale Neuroimaging Diagnosis [22.017120252054625]
  We propose a novel federated learning framework tailored for neuroimaging CAD systems.<n>Our approach includes a dynamic navigation module that routes samples to the most suitable local models.<n>We evaluated our framework using fMRI data from over 1300 MDD patients and 1100 healthy controls.
  arXiv  Detail & Related papers  (2025-08-08T07:19:49Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI [0.0]
  This study proposed an artificial intelligence framework for multiclass stroke classification using CT scan images.<n>The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification.<n>To enhance model generalization and address class imbalance, we applied data augmentation techniques.
  arXiv  Detail & Related papers  (2025-07-13T13:50:50Z)
• Towards a general-purpose foundation model for fMRI analysis [58.06455456423138]
  We introduce NeuroSTORM, a framework that learns from 4D fMRI volumes and enables efficient knowledge transfer across diverse applications.<n>NeuroSTORM is pre-trained on 28.65 million fMRI frames (>9,000 hours) from over 50,000 subjects across multiple centers and ages 5 to 100.<n>It outperforms existing methods across five tasks: age/gender prediction, phenotype prediction, disease diagnosis, fMRI-to-image retrieval, and task-based fMRI.
  arXiv  Detail & Related papers  (2025-06-11T23:51:01Z)
• From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction [38.67693323186832]
  This survey systematically categorizes existing approaches into four main categories: template models, statistical models, generative models, and implicit models.<n>We provide an extensive evaluation of these methods across various anatomical applications, from cardiac imaging to neurological studies.<n>The survey identifies current challenges in the field, including requirements for topological correctness, geometric accuracy, and multi-modality integration.
  arXiv  Detail & Related papers  (2025-05-06T15:01:43Z)
• Automated Radiology Report Generation: A Review of Recent Advances [5.965255286239531]
  Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation. Recent advances in artificial intelligence have demonstrated great potential for automatic radiology report generation.
  arXiv  Detail & Related papers  (2024-05-17T15:06:08Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Domain Shift in Computer Vision models for MRI data analysis: An Overview [64.69150970967524]
  Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use. Poor transferability of themodels to data from different sources or acquisition domains is one of the reasons for that.
  arXiv  Detail & Related papers  (2020-10-14T16:34:21Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.