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

• URL: http://arxiv.org/abs/2202.03430v1
• Date: Mon, 7 Feb 2022 01:33:01 GMT
• Title: A Topology-Attention ConvLSTM Network and Its Application to EM Images
• Authors: Jiaqi Yang, Xiaoling Hu, Chao Chen, and Chialing Tsai
• Abstract summary: We propose a novel TopologyAttention ConvLSTM Network (TACNet) for 3D image segmentation. Specifically, we propose a Spatial Topology-Attention (STA) module to process a 3D image as a stack of 2D image slices. In order to effectively transfer topology-critical information across slices, we propose an Iterative-Topology Attention (ITA) module.
• Score: 11.081936935096873
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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