Augmented reality display of neurosurgery craniotomy lesions based on feature contour matching

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...

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Bibliographic Details
Published in:Cognitive computation and systems 2021-09, Vol.3 (3), p.221-228
Main Authors: Zhang, Hao, Sun, Qi‐Yuan, Liu, Zhen‐Zhong
Format: Article
Language:English
Subjects:
Abdomen
Accuracy
Augmented reality
Blood vessels
brain
Breast surgery
Computed tomography
computerised tomography
Heart surgery
Image processing
image segmentation
Laparoscopy
Lesions
medical computing
medical image processing
Navigation systems
Neurosurgery
Patients
Physicians
Planning
Skull
Surgeons
Visualization
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Summary: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.
ISSN:2517-7567
1873-9601
2517-7567
1873-961X
DOI:10.1049/ccs2.12021