Abstract

Info

Title:: PAC-UNet: a brain tumor segmentation network integrating dual-path convolution and attention mechanism

Author(s):: brain tumor segmentation dual-path convolution pinwheel convolution dynamic kernel convolution attention mechanism U-Net; 1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China 2. Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China

Keywords:: brain tumor segmentation dual-path convolution pinwheel convolution dynamic kernel convolution attention mechanism U-Net

Abstract:: To address the issues of blurred lesion boundaries in brain tumor MRI images, insufficient segmentation sensitivity for small targets, and limited ability to extract multi-scale contextual information, this study proposes a U-shaped segmentation network named PAC-UNet, which integrates a dual-path convolution module and a hybrid attention mechanism module. The network uses a novel dual-path convolution module to replace standard convolutions, enabling parallel fusion of dynamic kernel convolution and pinwheel convolution paths. In the dynamic kernel convolution path, a radial basis function kernel generator replaces traditional fixed convolution kernels, which achieves dynamic kernel function adaptation through differentiable spline parameterization, thus balancing local detailed features and global semantic representations. The pinwheel convolution path adopts a four-parallel structure to effectively expand the receptive field and enhance the ability to capture contextual information. Meanwhile, a channel-spatial dual attention mechanism is used to fuse dual-path features, with a focus on key features while suppressing redundant background interference, thereby collaboratively optimizing multi-scale feature representations. Experiment results on the BraTS2023 dataset show that PAC-UNet achieves significant improvements in brain tumor segmentation performance.