GMAlignNet: multi-scale lightweight brain tumor image segmentation with enhanced semantic information consistency

Phys Med Biol. 2024 Apr 24. doi: 10.1088/1361-6560/ad4301. Online ahead of print.ABSTRACTAlthough the U-shaped architecture, represented by UNet, has become a major network model for brain tumor segmentation, the repeated convolution and sampling operations can easily lead to the loss of crucial information. Additionally, directly fusing features from different levels without distinction can easily result in feature misalignment, affecting segmentation accuracy. On the other hand, traditional convolutional blocks used for feature extraction cannot capture the abundant multi-scale information present in brain tumor images. This paper proposes a multi-scale feature-aligned segmentation model called GMAlignNet that fully utilizes Ghost convolution to solve these problems. Ghost Hierarchical Decoupled Fusion Unit and Ghost Hierarchical Decoupled Unit are used instead of standard convolutions in the encoding and decoding paths. This transformation replaces the holistic learning of volume structures by traditional convolutional blocks with multi-level learning on a specific view, facilitating the acquisition of abundant multi-scale contextual information through low-cost operations. Furthermore, a feature alignment unit is proposed that can utilize semantic information flow to guide the recovery of upsampled features. It performs pixel-level semantic information correction on misaligned features due to feature fusion. The proposed method is also employed to optimize three classic n...
Source: Physics in Medicine and Biology - Category: Physics Authors: Source Type: research