Brain vessel segmentation in contrast-enhanced T1-weighted MR Images for deep brain stimulation of the anterior thalamus using a deep convolutional neural network

Abstract

Deep brain stimulation (DBS) has been recently approved by the FDA to treat epilepsy patients with refractory seizures, i.e., patients for whom medications are not effective. It involves stimulating the anterior nucleus of the thalamus (ANT) with electric impulses using permanently placed electrodes. One main challenge with the procedure is to determine a trajectory to place the implant at the proper location while avoiding sensitive structures. In this work, we focus on one category of sensitive structures, i.e., brain vessels, and we propose a method to segment them in clinically acquired contrast-enhanced T1-weighted (T1CE) MRI images. We develop a deep-learning-based 3D U-Net model that we train/test on a set of images for which we have created the ground truth. We compare this approach to a traditional vesselness-based technique and we show that our method produces significantly better results (Dice score: 0.794), especially for small vessels.