Open AccessAugmented reality display of neurosurgery craniotomy lesions based on feature contour matching
Author(s) -
Zhang Hao,
Sun QiYuan,
Liu ZhenZhong
Publication year - 2021
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs2.12021
Subject(s) - craniotomy , augmented reality , computer science , zoom , feature (linguistics) , computer vision , artificial intelligence , segmentation , neurosurgery , matching (statistics) , interface (matter) , radiology , medicine , engineering , pathology , linguistics , philosophy , bubble , maximum bubble pressure method , parallel computing , petroleum engineering , lens (geology)
Abstract Traditional neurosurgical craniotomy primarily uses two‐dimensional cranial medical images to estimate the location of a patient’s intracranial lesions. Such work relies on the experience and skills of the doctor and may result in accidental injury to important intracranial physiological tissues. To help doctors more intuitively determine patient lesion information and improve the accuracy of surgical route formulation and craniotomy safety, an augmented reality method for displaying neurosurgery craniotomy lesions based on feature contour matching is proposed. This method uses threshold segmentation and region growing algorithms to reconstruct a 3‐D Computed tomography image of the patient’s head. The augmented reality engine is used to adjust the reconstruction model’s relevant parameters to meet the doctor’s requirements and determine the augmented reality matching method for feature contour matching. By using the mobile terminal to align the real skull model, the virtual lesion model is displayed. Using the designed user interface, doctors can view the patient’s personal information and can zoom in, zoom out, and rotate the virtual model. Therefore, the patient’s lesions information can be visualized accurately, which provides a visual basis for preoperative preparation.