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Automatic diagnosis of pneumothorax with M-mode ultrasound images based on D-MPL

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

To address the difficulties of M-mode ultrasound images classification in pneumothorax diagnosis and the shortcomings of existing neural network algorithms in this field, we proposed an M-mode ultrasound images classification model based on Disturbed Meta-Pseudo-Labels (D-MPL).

Methods

An M-mode ultrasound image augmentation system was designed to make the model more robust and generalizable. In D-MPL, teacher-generated pseudo-labeling was first taught to students through a soft mask, and additional disturbance data were added to the teacher network. As the loss of the teacher network continues to decline, disturbance data were injected to improve the generalization of the model to cope with image differences across patients in clinical settings.

Results

We compared the proposed model with four commonly used models, including MPL, EfficientnetB2, Inception V3, and Resnet101, in order to confirm its efficacy. Our model has an average specificity of 98.28%, sensitivity of 98.22%, F1-score of 98.23%, and AUC of 98.10%, according to the experiment findings, and its comprehensive performance is better than the above four models.

Conclusion

The results demonstrated our model's superiority over the competition and its greater. The model proposed in this study is expected to assist doctors in the diagnosis of pneumothorax as an auxiliary mean.

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Acknowledgements

M-mode ultrasound image data used in this study are all from the Third Affiliated Hospital of Soochow University.

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Authors and Affiliations

  1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Tao Zhang, Shiju Yan, Linying Yang & Tianxiang Yu

  2. Naval Medical Department, Naval Medical University, Shanghai, 200433, China

    Gaofeng Wei

  3. Department of Ultrasound, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, Jiangsu, China

    Yibo Ma

Authors
  1. Tao Zhang
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  2. Shiju Yan
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  3. Gaofeng Wei
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  4. Linying Yang
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  5. Tianxiang Yu
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Corresponding authors

Correspondence to Shiju Yan or Gaofeng Wei.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Informed consent was obtained from all participants included in the study.

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Cite this article

Zhang, T., Yan, S., Wei, G. et al. Automatic diagnosis of pneumothorax with M-mode ultrasound images based on D-MPL. Int J CARS 18, 303–312 (2023). https://doi.org/10.1007/s11548-022-02765-2

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  • DOI: https://doi.org/10.1007/s11548-022-02765-2

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