  Structural accuracy of segmentation is important for finescale structures in
biomedical images. We propose a novel TopologyAttention ConvLSTM Network
(TACNet) for 3D image segmentation in order to achieve high structural accuracy
for 3D segmentation tasks. Specifically, we propose a Spatial
Topology-Attention (STA) module to process a 3D image as a stack of 2D image
slices and adopt ConvLSTM to leverage contextual structure information from
adjacent slices. In order to effectively transfer topology-critical information
across slices, we propose an Iterative-Topology Attention (ITA) module that
provides a more stable topology-critical map for segmentation. Quantitative and
qualitative results show that our proposed method outperforms various baselines
in terms of topology-aware evaluation metrics.
