Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866909201295474688 |
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| author | Alkhimova, Svitlana Diumin, Oleksii |
| author_facet | Alkhimova, Svitlana Diumin, Oleksii |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2303_02518 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing Alkhimova, Svitlana Diumin, Oleksii Image and Video Processing Machine Learning 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. |
| title | Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2303.02518 |