A Topological-Attention ConvLSTM Network and Its Application to EM Images

JIAQI YANG, Xiaoling Hu, Chao Chen, Chia-Ling Tsai

Abstract:

Structural accuracy of segmentation is important for fine-scale structures in biomedical images. We propose a novel Topological-Attention
ConvLSTM Network (TACLNet) for 3D anisotropic image segmentation with high structural accuracy. We adopt ConvLSTM to leverage contextual information from adjacent slices while achieving high efficiency. We propose a Spatial Topological-Attention (STA) module to effectively transfer topologically critical information across slices. Furthermore, we propose an Iterative Topological-Attention (ITA) module that provides a more stable topologically critical map for segmentation. Quantitative and qualitative results show that our proposed method outperforms various baselines in terms of topology-aware evaluation metrics.