Cerebrovascular segmentation of TOF-MRA based on seed point detection and multiple-feature fusion.

The accurate extraction of cerebrovascular structures from time-of-flight (TOF) data is important for diagnosis of cerebrovascular diseases and planning and navigation of neurosurgery. In this study, we proposed a cerebrovascular segmentation method based on automatic seed point detection and vascular multiple-feature fusion. First, the brain mask in the T1-MR image is detected to enable the extraction of the TOF brain structure by simultaneously acquiring the TOF image and its corresponding T1-MRI.

An automatic markerless registration method for neurosurgical robotics based on an optical camera.

Purpose: Current markerless registration methods for neurosurgical robotics use the facial surface to match the robot space with the image space, and acquisition of the facial surface usually requires manual interaction and constrains the patient to a supine position. To overcome these drawbacks, we propose a registration method that is automatic and does not constrain patient position.

Methods: An optical camera attached to the robot end effector captures images around the patient's head from multiple views.

Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features.

Background And Objective: In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset.

Methods: First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved.