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MSCA-Sp R-CNN: a segmentation algorithm for pneumonia small lesions integrating multi-scale channel attention and sub-pixel upsampling

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Abstract

Computed Tomography (CT) imaging has been widely employed as a critical tool in clinical diagnosis. In recent years, deep learning and image segmentation techniques have been increasingly applied to the identification of lesions in CT images, particularly for pulmonary inflammatory damage caused by various viruses. Certain diseases present honeycomb-like lesions, which are challenging to accurately identify due to their variable morphology and uncertain localization. Furthermore, the small scale of these honeycomb lesions makes precise segmentation even more difficult. To address these challenges, we leveraged prior knowledge of the radiological scale features of the lesions and proposed a preprocessing strategy tailored to honeycomb lung datasets. This strategy aims to eliminate redundant and non-informative data while facilitating instance segmentation. Building on this, we introduced a local lesion copy-paste data augmentation algorithm to ensure that lesions are accurately placed within the pulmonary region while increasing the data volume. To tackle the small-scale characteristics of honeycomb lung lesions, we designed the MSCA-Sp R-CNN model, an instance segmentation framework that integrates a multi-scale channel attention module and a dual sub-pixel convolution upsampling module. Experimental results on the CC-CCII and UESTC-COVID-19 part2 datasets demonstrate that the proposed strategy and model significantly improve the accuracy of honeycomb lung lesion segmentation. Moreover, the visualization analysis highlights the model’s superior ability to identify multi-scale lesions, particularly small lesions. Compared to other algorithms, the proposed approach exhibits more competitive performance.

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Acknowledgment

This work was supported by the Beijing Natural Science Foundation (QY24306) and the Research Fund of Jiaxing Key Laboratory of Smart Transportation Open Project Funding (ZHJT202304).

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Authors and Affiliations

  1. College of Information Engineering, Capital Normal University, West Third Ring Road North, Beijing, 100048, China

    Jiacheng Lu, Kaiwen Wang, Hui Ding & Zhuhong Shao

  2. Jiaxing Key Laboratory of Smart Transportations, Changsheng South Road, Jiaxing, 314001, China

    Zhuhong Shao

  3. Jiading Branch of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Huangjiahuayuan Road, Shanghai, 201803, China

    Rongying Qin

  4. School of Artificial Intelligence, China University of Mining and Technology-Beijing, Xueyuan Road, Beijing, 100083, China

    Guoping Huo

Authors
  1. Jiacheng Lu
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Contributions

All authors contributed to the study's conception and design. H.D. and J.L. wrote the main manuscript text, conducted the primary experiments, and analyzed the results. G.H. contributed to the result analysis and manuscript revision. K.W. was responsible for preparing figures 1-3, data collection, and preprocessing. Z.S. and R.Q. assisted with the experimental design and contributed to manuscript editing. All authors reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence to Hui Ding or Guoping Huo.

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The authors declare no competing interests.

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Communicated by Bing-kun Bao.

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Lu, J., Wang, K., Ding, H. et al. MSCA-Sp R-CNN: a segmentation algorithm for pneumonia small lesions integrating multi-scale channel attention and sub-pixel upsampling. Multimedia Systems 31, 149 (2025). https://doi.org/10.1007/s00530-025-01726-4

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