Automated Marker Localization in the Planning Phase of Robotic Neurosurgery

Abstract

Accurate patient registration is a critical issue in medical image-guided interventions. The neurosurgical robotic system RObotic Neuro-NAvigation (RONNA) uses four retro-reflective spheres, on a marker attached to the patient's cranial bone, for patient registration in physical and image space. In this paper, the algorithm for automatic localization of spherical fiducials in CT scans is presented and clinically evaluated. The developed localization algorithm uses a unique approach, which combines machine vision algorithms, biomedical image filtration methods, and mathematical estimation methods. The performance of the localization algorithm was evaluated in comparison with four skilled human operators. The measurements were based on twelve patient and eight lab phantom CT scans. The localization error of the algorithm in comparison with the human readings was smaller by 49.29% according to the ground truth estimation and by 45.91% according to the intra-modal estimation. Localization processing time was reduced by 84.96%. Reliability in terms of successful localization of the fiducial marker was 100% for 20 different test samples containing a total of 116 spherical fiducials. Based on the tests carried out in clinical conditions, the localization algorithm has demonstrated reliability with a high degree of accuracy and short processing time. The developed algorithm provides fully automated and accurate machine vision-based patient localization for the neurosurgical clinical application of the robotic system RONNA.

Keywords

• Accuracy
• biomedical image processing
• clinical trials
• DICOM
• medical robotics
• stereotaxy