Background: Due to the significant variances in their shape and size, it is a challenging
task to automatically segment gliomas. To improve the performance of glioma segmentation tasks,
this paper proposed a multilevel attention pyramid scene parsing network (MLAPSPNet) that aggregates
the multiscale context and multilevel features.
Methods: First, T1 pre-contrast, T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1
post-contrast sequences of each slice are combined to form the input. Afterwards, image normalization
and augmentation techniques are applied to accelerate the training process and avoid overfitting,
respectively. Furthermore, the proposed MLAPSPNet that introduces multilevel pyramid pooling
modules (PPMs) and attention gates is constructed. Eventually, the proposed network is compared
with some existing networks.
Results: The dice similarity coefficient (DSC), sensitivity and Jaccard score of the proposed system
can reach 0.885, 0.933 and 0.8, respectively. The introduction of multilevel pyramid pooling
modules and attention gates can improve the DSC by 0.029 and 0.022, respectively. Moreover,
compared with Res-UNet, Dense-UNet, residual channel attention UNet (RCA-UNet), DeepLab
V3+ and UNet++, the DSC is improved by 0.032, 0.026, 0.014, 0.041 and 0.011, respectively.
Conclusion: The proposed multilevel attention pyramid scene parsing network can achieve stateof-
the-art performance, and the introduction of multilevel pyramid pooling modules and attention
gates can improve the performance of glioma segmentation tasks.