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Modeling the sequential information of image sequences has been a vital stepof various vision tasks and convolutional long short-term memory (ConvLSTM) hasdemonstrated its superb performance in such spatiotemporal problems.Nevertheless, the hierarchical data structures in a significant amount of tasks(e.g., human body parts and vessel/airway tree in biomedical images) cannot beproperly modeled by sequential models. Thus, ConvLSTM is not suitable fortree-structured image data analysis. In order to address these limitations, wepresent tree-structured ConvLSTM models for tree-structured image analysistasks which can be trained end-to-end. To demonstrate the effectiveness of theproposed tree-structured ConvLSTM model, we present a tree-structuredsegmentation framework which consists of a tree-structured ConvLSTM and anattention fully convolutional network (FCN) model. The proposed framework isextensively validated on four large-scale coronary artery datasets. The resultsdemonstrate the effectiveness and efficiency of the proposed method.

Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding