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

arXiv: https://arxiv.org/abs/2303.02518 · HTML: https://mlsanity.com/paper/2303.02518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [Impact of perfusion ROI detection to the quality of CBV perfusion map](https://mlsanity.com/paper/1912.05471/) (2019)
- [Automated Detection of Regions of Interest for Brain Perfusion MR Images](https://mlsanity.com/paper/1902.06323/) (2019)
- [CUSUM Filter for Brain Segmentation on DSC Perfusion MR Head Scans with Abnormal Brain Anatomy](https://mlsanity.com/paper/1904.00787/) (2019)
- [Analysis of effectiveness of thresholding in perfusion ROI detection on T2-weighted MR images with abnormal brain anatomy](https://mlsanity.com/paper/1912.05469/) (2019)

## Abstract (arXiv)

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.