During a craniotomy, the skull is opened to allow surgeons to have access to the brain and perform the procedure. The position and size of this opening are chosen in a way to avoid critical structures, such as vessels, and facilitate the access to tumors. Planning the operation is done based on pre-operative images and does not account for intra-operative surgical events. We present a novel image-guided neurosurgical system to optimize the craniotomy opening. Using physics-based modeling we define a cortical deformation map that estimates the displacement field at candidate craniotomy locations. This deformation map is coupled with an image analogy algorithm that produces realistic synthetic images that can be used to predict both the geometry and the appearance of the brain surface before opening the skull. These images account for cortical vessel deformations that may occur after opening the skull and is rendered in a way that increases the surgeon's understanding and assimilation. Our method was tested retrospectively on patients data showing good results and demonstrating the feasibility of practical use of our system.
Keyphrases
- deep learning
- convolutional neural network
- resting state
- white matter
- optical coherence tomography
- end stage renal disease
- artificial intelligence
- machine learning
- cerebral ischemia
- functional connectivity
- chronic kidney disease
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- multiple sclerosis
- electronic health record
- big data
- patient reported outcomes
- minimally invasive
- mass spectrometry
- brain injury
- patient reported
- robot assisted