Pool-UNet: Ischemic Stroke Segmentation from CT Perfusion Scans Using Poolformer UNet

Abstract

Ischemic strokes are the most common acute brain disorder, and seriously threaten patients’ lives. In order to help physicians determine the location of ischemic stroke lesions and other information as early as possible, many scholars have used convolutional neural networks and Transformer segmentation networks to segment lesions on CT perfusion images. However, convolutional neural networks are not capable of extracting spatial information sufficiently, which leads to loss of effective lesion information. In addition, the global attention mechanism module of Transformer is computationally intensive at runtime, which is not suitable for use in high-resolution input and intensive prediction tasks. We designed a DSE-ResNet module to solve these problems to establish spatial channel information correlation. Then we innovatively propose the Pool-UNet model, which combines the Poolformer structure with a convolutional neural network. It can efficiently model the global context and learn multi-scale features while maintaining a grasp of the lowlevel details. The segmentation results on the ISLES-2018 dataset show that PoolUNet achieves 67.82% precision, 56.54% recall, 56.04% Dice coefficient, and 21.14 mm Haushofer distance. Compared with the classical UNet, R2UNet, and TransUNet 3 segmentation models, Pool-UNet improved at least 0.26%, 1.52%, 1.07%, and 0. 17mm in accuracy, recall, Dice coefficient, and Hausdorff distance, respectively. Pool-UNet has a competitive advantage over other classical and advanced medical segmentation algorithms.