Data-Driven Registration and Modeling of Brain Deformation for Image-Guided Neurosurgery: A Systematic Review 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- 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
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-07-10
摘要
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 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.