A Segmentation Method of Foramen Ovale Based on Multiatlas

Abstract

Trigeminal neuralgia is a neurological disease. It is often treated by puncturing the trigeminal nerve through the skin and the oval foramen of the skull to selectively destroy the pain nerve. The process of puncture operation is difficult because the morphology of the foramen ovale in the skull base is varied and the surrounding anatomical structure is complex. Computer-aided puncture guidance technology is extremely valuable for the treatment of trigeminal neuralgia. Computer-aided guidance can help doctors determine the puncture target by accurately locating the foramen ovale in the skull base. Foramen ovale segmentation is a prerequisite for locating but is a tedious and error-prone task if done manually. In this paper, we present an image segmentation solution based on the multiatlas method that automatically segments the foramen ovale. We developed a data set of 30 CT scans containing 20 foramen ovale atlas and 10 CT scans for testing. Our approach can perform foramen ovale segmentation in puncture operation scenarios based solely on limited data. We propose to utilize this method as an enabler in clinical work.

Parcellation of Brain Magnetic Resonance Images using Improved Atlases Selection Model

“…The strategy applied when multiple atlases are deployed is to generate multiple parcellations of the same image using these atlases and systematically combine these parcellations in a multi-classifier framework to get a final unique parcellation [22,23]. It has been established that even though single or multiple atlas could be used, usage of multiple atlas is more accurate than using single atlas [24,3,4,20,25,26].…”