Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification

After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.

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Publication Details

Journal
Frontiers in Neuroscience
Published
2024-09-30
DOI
https://doi.org/10.3389/fnins.2024.1416174
Citations
4
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
1.00
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article

Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification

Mousa Shamsi, Sebelan Danishvar, Abolhassan Shakeri Bavil, Mahdi Bashiri Bawil
4 citations
Frontiers in Neuroscience
Medical Image Segmentation Techniques
1.00
article

Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification

Mousa Shamsi, Sebelan Danishvar, Abolhassan Shakeri Bavil, Mahdi Bashiri Bawil
article en
4 citations

Abstract

After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.

Frontiers in NeuroscienceVol. 18
Sahand University of Technology (IR), Tabriz University of Medical Sciences (IR), Brunel University of London (GB)
Openalex Percentile: Top 22%
Medical Image Segmentation Techniques
1.00
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