Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

Objective: To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).

Methods: Vessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straighten, and resample the interested vessel segment into 2-D slices. A U-net-like fully convolutional networks (FCN) method was proposed for automated vessel wall segmentation by hierarchical extraction of low- and high-order convolutional features.

Results: The network was trained and validated on 1160 slices and tested on 545 slices. The proposed segmentation method demonstrated satisfactory agreement with manual segmentations with Dice coefficient of 0.89 for the lumen and 0.77 for the vessel wall. The method was further applied to a clinical study of additional 12 symptomatic and 12 asymptomatic patients with >50% ICAD stenosis at the middle cerebral artery (MCA). Normalized wall index at the focal MCA ICAD lesions was found significantly larger in symptomatic patients compared to asymptomatic patients.

Conclusion: We have presented an automated vessel wall segmentation method based on FCN as well as the IVA framework for 3-D intracranial MR VWI.

Significance: This approach would make large-scale quantitative plaque analysis more realistic and promote the adoption of MR VWI in ICAD management.