Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing

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dc.contributor.authorAlkhimova, Svitlana Mykolaivna
dc.contributor.authorDiumin, Oleksii Dmytrovych
dc.date.accessioned2023-02-01T10:42:04Z
dc.date.available2023-02-01T10:42:04Z
dc.date.issued2022
dc.description.abstractenAccurate skull-stripping is crucial preprocessing in dynamic susceptibility contrast-enhanced perfusion magnetic resonance data analysis. The presence of non-brain tissues impacts the perfusion parameters assessment. In this study, we propose different integration strategies for the spatial and channel squeeze and excitation attention mechanism into the baseline U-Net+ResNet neural network architecture to provide automatic skull-striping i.e., Standard scSE, scSE-PRE, scSE-POST, and scSE Identity strategies of plugging of scSE block into the ResNet backbone. We comprehensively investigate the performance of skull-stripping in Т2* weighted MR images with abnormal brain anatomy. The comparison that utilizing any of the proposed strategies provides the robustness of skull-stripping. However, the scSE-POST integration strategy provides the best result with an average Dice Coefficient of 0.9810 +/- 0.006.uk
dc.format.pagerangeP. 549-555.uk
dc.identifier.citationAlkhimova, S. Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing / Alkhimova Svitlana, Diumin Oleksii // Proceedings of the XII International Scientific and Practical Conference «Current challenges, trends and transformations», December 13-16, 2022, Boston, USA. - Boston : International Science Group, 2022. - P. 549-555.uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/52245
dc.language.isoenuk
dc.publisherInternational Science Groupuk
dc.publisher.placeBostonuk
dc.sourceProceedings of the XII International Scientific and Practical Conference «Current challenges, trends and transformations», December 13-16, 2022, Boston, USAuk
dc.subjectskull-stripinguk
dc.subjectbrainuk
dc.subjectsegmentationuk
dc.subjectregion of interestuk
dc.subjectdeep neural networkuk
dc.subjectdynamic susceptibility contrast perfusionuk
dc.subjectmagnetic resonance imaginguk
dc.titleAttention-based convolutional neural network for perfusion T2-weighted MR images preprocessinguk
dc.typeArticleuk

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