3D hierarchical dual-attention fully convolutional networks with hybrid losses for diverse glioma segmentation

Abstract Accurate glioma segmentation based on magnetic resonance imaging (MRI) is crucial for assisting with the diagnosis of gliomas. However, the manual delineation of all diverse gliomas, including the whole tumors (WTs), tumor cores (TCs) and enhancing tumors (ETs) of high-grade gliomas (HGG) and low-grade gliomas (LGG), is laborious and often error prone. The different phenotypes, sizes and locations of gliomas in/between patients make automatic segmentation a challenging task. To alleviate these challenges, in this paper, we propose a 3D fully convolutional network (FCN) with a dual-attention (i.e., global and local attention) mechanism to segment diverse gliomas simultaneously. The global attention mechanism (GAM) focuses on segmenting gliomas precisely by segment discrimination learning with a weight-allocated segmentation loss function to alleviate biased results obtained for tumors with large sizes and an adversarial loss function to refine the segmentations of areas with low contrast relative to their neighbors. The local attention mechanism (LAM) constantly revises effective features with the guidance of a united loss function at different levels. Furthermore, we present a hierarchical feature module (HFM) with a weight-sharing block to obtain more information about the boundaries of different scales, aiming at enhancing the learning of ambiguous tumor outlines. According to experimental results, our network outperforms ten state-of-the-art methods. Ablation studies show that the proposed model components are effective for diverse glioma segmentation.