| Autor: |
Yi Lu, Bin Kong, Feng Gao, Kunlin Cao, Siwei Lyu, Shaoting Zhang, Shu Hu, Youbing Yin, Xin Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Frontiers in Physics, Vol 11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2296-424X |
| DOI: |
10.3389/fphy.2023.1095277 |
| Popis: |
Modeling sequential information for image sequences is a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb performance in such spatiotemporal problems. Nevertheless, the hierarchical data structures (e.g., human body parts and vessel/airway tree in biomedical images) in various tasks cannot be properly modeled by sequential models. Thus, ConvLSTM is not suitable for analyzing tree-structured image data that has a rich relation among its elements. In order to address this limitation, we present a tree-structured ConvLSTM model for tree-structured image analysis which can be trained end-to-end. To demonstrate its effectiveness, we first evaluate the proposed tree-structured ConvLSTM model on a synthetic Tree-Moving-MNIST dataset for tree-structured modeling. Experimental results demonstrate the superiority of the tree-structured ConvLSTM model for tree-structured image analysis compared with other alternatives. Additionally, we present a tree-structured segmentation framework which consists of a tree-structured ConvLSTM layer and an attention fully convolutional network (FCN) model. The proposed framework is validated on four large-scale coronary artery datasets. The results demonstrate the effectiveness and efficiency of the proposed method, showing its potential use cases in the analysis of tree-structured image data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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