Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

ABSTRACT:

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

SUBMITTER: Shi F 

PROVIDER: S-EPMC6788976 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature