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AE-UNet: a composite lung CT image segmentation framework using attention mechanism and edge detection

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Abstract

The primary impediments in lung CT image segmentation stem from the ambiguity in edge definition and the inadequate segmentation accuracy. Addressing these issues, this paper introduces a novel composite lung CT image segmentation framework that integrates an attention mechanism with an edge detection operator. We utilize residual dynamic convolutions as the encoder to augment the network's capability for extracting and representing nuanced lesion features. Sobel edge detection is integrated into the skip connections to facilitate the transmission and utilization of edge information. In particular, we introduce an information fusion attention module for deeper layers, optimizing feature reorganization and utilization by attention mechanisms and dilated convolution. Experimental evaluations on two lung CT datasets reveal that our proposed AE-UNet achieves outstanding segmentation performance, surpassing the best baseline network by an average of 0.93%.

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Acknowledgements

This work is supported by the Chengdu University Pattern Recognition and Intelligent Information Processing Sichuan University Key Laboratory open fund (MSSB-2024-13), the Foundation of Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument (2020B1212060077), Chengdu University Tianfu Culture Digital Innovation Key Laboratory of Sichuan Culture and Tourism Department open project (TFWH-2024-5).

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Authors and Affiliations

  1. School of Computer Science, Chengdu University, Chengdu, China

    Hongzhi Li, Zhanghao Ren, Guoqing Zhu & Jiaxi Wang

  2. Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu University, Chengdu, China

    Jiaxi Wang

  3. Key Laboratory of Digital Innovation of Tianfu Culture, Sichuan Provincial Department of Culture and Tourism, Chengdu University, Chengdu, China

    Jiaxi Wang

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  1. Hongzhi Li
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H.L. and J.W. wrote the main manuscript text, and Z.R. prepared figures. All authors reviewed the manuscript.

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Correspondence to Jiaxi Wang.

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Li, H., Ren, Z., Zhu, G. et al. AE-UNet: a composite lung CT image segmentation framework using attention mechanism and edge detection. J Supercomput 81, 331 (2025). https://doi.org/10.1007/s11227-024-06874-4

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