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Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets.

Jumpei Nitta, Megumi Nakao, Keiho Imanishi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
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    This study introduces a U-net based method for segmenting lungs during endoscopic surgery. Texture translation using CycleGAN improved accuracy for smoker lungs, enhancing 3D structure understanding.

    Area of Science:

    • Medical Imaging
    • Surgical Technology
    • Artificial Intelligence in Medicine

    Background:

    • Accurate 3D understanding of organs is crucial for safe endoscopic surgery.
    • Organs deforming during surgery require intraoperative estimation, necessitating 2D segmentation from camera images.
    • Existing methods struggle with the significant deformation of organs like the lungs.

    Purpose of the Study:

    • To propose a novel region segmentation method for the lung using U-net for endoscopic surgery.
    • To address the challenge of accurate lung segmentation, particularly for smoker lungs.
    • To improve the understanding of the three-dimensional structure of the lung during surgery.

    Main Methods:

    • Utilized a U-net architecture for lung region segmentation.

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  • Employed CycleGAN for texture translation to enhance lung surface characteristics.
  • Applied the method to endoscopic camera images of lungs.

    • Main Results:

    • Achieved accurate 2D region segmentation of the lung.
    • Demonstrated improved segmentation accuracy for smoker lungs compared to standard methods.
    • Successfully translated lung surface textures to enhance segmentation.

    Conclusions:

    • The proposed U-net and CycleGAN approach effectively segments lungs during endoscopic surgery.
    • Texture translation is a viable strategy to improve segmentation accuracy for challenging cases like smoker lungs.
    • This method contributes to enhanced safety and precision in lung surgeries.