Sulcal Pattern Matching with the Wasserstein Distance

We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matchin...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 1 - 5
Main Authors Chen, Zijian, Das, Soumya, Chung, Moo K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.04.2023
Subjects
Brain modeling
Computational modeling
Deformation
Image registration
Magnetic resonance
Mathematical models
Pattern matching
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ISSN1945-8452
DOI10.1109/ISBI53787.2023.10230413

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Summary:We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matching a challenge. We work out the mathematical details and develop the gradient descent algorithms for estimating the deformation field. We further quantify the image registration performance. This method is applied in identifying the differences between male and female sulcal patterns.
ISSN:1945-8452
DOI:10.1109/ISBI53787.2023.10230413