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arXiv · 2202.03430

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

Jiaqi Yang·Xiaoling Hu·Chao Chen·Chialing Tsai
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Abstract

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.

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Jiaqi Yang, Xiaoling Hu, Chao Chen, Chialing Tsai. 2022-02-07. A Topology-Attention ConvLSTM Network and Its Application to EM Images. https://arxiv.org/abs/2202.03430

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