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

神经外科手术脑部变形建模综述:学习方法显示出潜力但面临临床挑战

本系统性综述考察了用于影像引导的神经外科手术中脑部变形配准与建模的数据驱动方法,重点关注 2020 年至 2025 年间开发的基于学习的方法。研究人员分析了来自主要数据库的 46 篇符合条件的论文,将方法学分为图像配准的深度学习、直接变形场回归和混合模型等类别。虽然这些方法在准确性和效率方面显示出潜力,但在鲁棒性、标准化基准测试、可解释性和临床就绪性方面仍存在挑战。 AI

影响 强调了人工智能在医学影像和手术引导方面的进展,同时指出了临床应用的局限性。

排序理由 该条目是一篇在 arXiv 上发表的系统性综述论文。[lever_c_demoted from research: ic=1 ai=1.0]

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神经外科手术脑部变形建模综述:学习方法显示出潜力但面临临床挑战

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该条目是一篇在 arXiv 上发表的系统性综述论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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 ·

    面向图像引导神经外科的数据驱动脑部变形注册与建模:系统性综述

    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 …