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

Can Cui; Han Liu; Dario J. Englot; Benoit M. Dawant

doi:10.1117/12.2581896

15 February 2021 Brain vessel segmentation in contrast-enhanced T1-weighted MR Images for deep brain stimulation of the anterior thalamus using a deep convolutional neural network

Can Cui, Han Liu, Dario J. Englot, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115980K (2021) https://doi.org/10.1117/12.2581896
Event: SPIE Medical Imaging, 2021, Online Only

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.

Conference Presentation

Citation Download Citation

Can Cui, Han Liu, Dario J. Englot, and Benoit M. Dawant "Brain vessel segmentation in contrast-enhanced T1-weighted MR Images for deep brain stimulation of the anterior thalamus using a deep convolutional neural network", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980K (15 February 2021); https://doi.org/10.1117/12.2581896

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