论文标题
拓扑注意的Convlstm网络及其在EM图像中的应用
A Topology-Attention ConvLSTM Network and Its Application to EM Images
论文作者
论文摘要
分割的结构准确性对于生物医学图像中的罚款结构很重要。我们为3D图像分割提出了一种新颖的拓扑探测弯曲网络（TACNET），以实现3D分割任务的高结构准确性。具体而言，我们提出了一个空间拓扑注意事项（STA）模块，以处理3D图像作为2D图像切片的堆栈，并采用Convlstm来利用相邻切片的上下文结构信息。为了有效地跨切片传输关键拓扑信息，我们提出了一个迭代式培养基关注（ITA）模块，该模块为分割提供了更稳定的关键拓扑 - 关键拓扑图。定量和定性结果表明，我们所提出的方法在拓扑感知评估指标方面优于各种基准。
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.
