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Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
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Related Experiment Video

Updated: Jan 29, 2026

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
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Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

Feng Shi, Qi Yang, Xiuhai Guo

    IEEE Transactions on Bio-Medical Engineering
    |February 5, 2019
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    An automated method using convolutional neural networks segments intracranial vessel walls in MR images. This facilitates quantitative analysis for intracranial atherosclerotic disease (ICAD) management.

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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence
    • Cardiovascular Imaging

    Background:

    • Intracranial atherosclerotic disease (ICAD) poses a significant stroke risk.
    • Accurate quantification of vessel wall changes is crucial for ICAD management.
    • Current manual segmentation of MR vessel wall images is time-consuming and subjective.

    Purpose of the Study:

    • To develop an automated vessel wall segmentation method using convolutional neural networks (CNNs).
    • To facilitate quantitative analysis of magnetic resonance (MR) vessel wall images in patients with ICAD.
    • To establish a framework for large-scale quantitative plaque analysis in ICAD.

    Main Methods:

    • Acquired whole-brain 3-D MR vessel wall imaging (VWI) data from 56 subjects.
    • Developed an intracranial vessel analysis (IVA) framework for vessel segment extraction and resampling.
    • Proposed a U-net-like fully convolutional networks (FCN) method for automated vessel wall segmentation.

    Main Results:

    • The FCN method achieved high agreement with manual segmentations (Dice coefficient 0.89 for lumen, 0.77 for vessel wall).
    • Applied the method to symptomatic and asymptomatic ICAD patients, revealing significantly larger normalized wall index in symptomatic individuals.
    • Demonstrated the method's effectiveness in a clinical study involving middle cerebral artery (MCA) stenosis.

    Conclusions:

    • An automated vessel wall segmentation method based on FCN and the IVA framework was successfully developed for 3-D intracranial MR VWI.
    • This automated approach enhances the feasibility of large-scale quantitative plaque analysis.
    • The method is expected to promote the wider adoption of MR VWI in managing ICAD.