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

• URL: http://arxiv.org/abs/2303.02518v1
• Date: Sat, 4 Mar 2023 22:40:59 GMT
• Title: Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing
• Authors: Svitlana Alkhimova, Oleksii Diumin
• Abstract summary: We propose different integration strategies for the spatial and channel squeeze and excitation attention mechanism into the baseline U-Net+ResNet neural network architecture. We investigate the performance of skull-stripping in T2-star weighted MR images with abnormal brain anatomy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate 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 T2-star 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.

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