TY - CHAP
T1 - Augmented Reality in Neurosurgery
AU - van Doormaal, Jesse A.M.
AU - van Doormaal, Tristan P.C.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/11/11
Y1 - 2024/11/11
N2 - Augmented Reality (AR) involves superimposing digital content onto the real environment. AR has evolved into a viable tool in neurosurgery, enhancing intraoperative navigation, medical education and surgical training by integrating anatomical data with the real world. Neurosurgical AR relies on several key techniques to be successful, which includes image segmentation, model rendering, AR projection, and image-to-patient registration. For each of these technical components, different solutions exist, with each having their own advantages and limitations. Intraoperative AR applications cover diverse neurosurgical disciplines including vascular, oncological, spinal, and functional surgeries. Preliminary studies indicate that AR may improve the understanding of complex anatomical structures and offer sufficient accuracy for use as a navigational tool. Additionally, AR shows promise in enhancing surgical training and patient education through interactive 3D models, aiding in the comprehension of intricate anatomical details. Despite its potential, the widespread adoption of AR in clinical settings depends on overcoming technical limitations and validating its clinical efficacy.
AB - Augmented Reality (AR) involves superimposing digital content onto the real environment. AR has evolved into a viable tool in neurosurgery, enhancing intraoperative navigation, medical education and surgical training by integrating anatomical data with the real world. Neurosurgical AR relies on several key techniques to be successful, which includes image segmentation, model rendering, AR projection, and image-to-patient registration. For each of these technical components, different solutions exist, with each having their own advantages and limitations. Intraoperative AR applications cover diverse neurosurgical disciplines including vascular, oncological, spinal, and functional surgeries. Preliminary studies indicate that AR may improve the understanding of complex anatomical structures and offer sufficient accuracy for use as a navigational tool. Additionally, AR shows promise in enhancing surgical training and patient education through interactive 3D models, aiding in the comprehension of intricate anatomical details. Despite its potential, the widespread adoption of AR in clinical settings depends on overcoming technical limitations and validating its clinical efficacy.
KW - Augmented reality
KW - Computer vision
KW - Image segmentation
KW - Image-to-patient registration
KW - Medical education
KW - Model rendering
KW - Neuronavigation
KW - Spatial computing
KW - Surgical training
UR - http://www.scopus.com/inward/record.url?scp=85208972966&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-64892-2_21
DO - 10.1007/978-3-031-64892-2_21
M3 - Chapter
AN - SCOPUS:85208972966
SN - 978-3-031-64891-5
T3 - Advances in Experimental Medicine and Biology
SP - 351
EP - 374
BT - Computational Neurosurgery
PB - Springer
ER -