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Automated Segmentation Based on Deep Learning of the MR Vessel Wall Imaging

Published: 06 October 2021 Publication History

Abstract

The rupture of unstable or vulnerable atherosclerotic plaque is the major cause of ischemic stroke. Manual analysis of the vessel wall and plaque is labor-intensive and experience-dependent. The purpose of this study is to develop an automatic method to segment the vessel wall and lumen contour for quantitative measurement. In this work, a CNN architecture for fully automated segmentation of arterial lumen and vessel wall on MR vessel wall images was developed and evaluated on ischemic stroke patients. In conclusion, we proposed a good performance automatic analysis method for the vessel wall and is important for plaque analysis.

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cover image ACM Other conferences
ICBDC '21: Proceedings of the 6th International Conference on Big Data and Computing
May 2021
218 pages
ISBN:9781450389808
DOI:10.1145/3469968
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 06 October 2021

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Author Tags

  1. Deep Learning
  2. MRI
  3. Segmentation
  4. Vessel Wall

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