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- W3136279231 abstract "Pulmonary vessel segmentation is an important precondition for diagnosis, quantitative analysis and treatment planning of pulmonary diseases. Recently, Convolutional Neural Networks (CNNs) has achieved great success in biomedical image processing. However, due to the complex structure, inter-vessel differences and clinical lesions, pulmonary vessel segmentation still faces great challenges. In this work, we propose a novel end-to-end pulmonary vessel segmentation network named Joint Multi-Scale and Dual Attention Gate Network (JMSDAGNet). The JMSDAGNet aggregate feature maps of different scales, which can effectively improve the segmentation accuracy of thin vessels. To focus on vascular areas and suppress the noise caused by diseases, we introduce attention mechanisms that can adaptively learn local and global information. We trained and validated the proposed JMSDAGNet in a custom dataset. Both quantitative and qualitative results of the comprehensive experiments prove the superiority of our proposed method in comparison with the state-of-the-art methods." @default.
- W3136279231 created "2021-03-29" @default.
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- W3136279231 date "2020-12-13" @default.
- W3136279231 modified "2023-10-16" @default.
- W3136279231 title "Joint Multi-Scale and Dual Attention Gate Network for Pulmonary Vessel Segmentation" @default.
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- W3136279231 doi "https://doi.org/10.1109/csrswtc50769.2020.9372459" @default.
- W3136279231 hasPublicationYear "2020" @default.
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