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Brain deformation modeling for neurosurgery reviewed: learning methods show promise but face clinical hurdles

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This systematic review examines data-driven methods for brain deformation registration and modeling in image-guided neurosurgery, focusing on learning-based approaches developed between 2020 and 2025. Researchers analyzed 46 eligible studies from major databases, categorizing methodologies such as deep learning for image registration, direct deformation field regression, and hybrid models. While these methods show promise in accuracy and efficiency, challenges remain in robustness, standardized benchmarking, interpretability, and clinical readiness. AI

IMPACT Highlights advancements in AI for medical imaging and surgical guidance, while noting limitations for clinical adoption.

RANK_REASON The item is a systematic review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Brain deformation modeling for neurosurgery reviewed: learning methods show promise but face clinical hurdles

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The item is a systematic review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · 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 ·

    Data-Driven Registration and Modeling of Brain Deformation for Image-Guided Neurosurgery: A Systematic Review

    arXiv:2602.10155v3 Announce Type: replace-cross 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 …

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