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首页> 外文期刊>Sensing and imaging >Automatic Nodule Segmentation Method for CT Images Using Aggregation‑U‑Net Generative Adversarial Networks
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Automatic Nodule Segmentation Method for CT Images Using Aggregation‑U‑Net Generative Adversarial Networks

机译：使用聚合 - U型净生成对冲网络自动结节CT图像分割方法

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摘要

Nodule segmentation plays a vital role in the detection and diagnosis for lung cancer. Nevertheless, manual segmentation by radiologists can be time-consuming and labor-intensive. In recent years, deep learning methods have performed well on medical image segmentation. Generative adversarial networks (GAN) are often used for image generation. In this paper, GAN is introduced to perform image segmentation, and a network called Aggregation-U-net GAN (AUGAN) is proposed, which is applied to automatically segment nodules in chest computed tomography (CT) images. The generator, which is modified by combining U-net with deep aggregation, learns features of lung nodules and makes the segmentation close to the ground truth. We used a method called tissue augmentation that 300 patches from normal areas in chest CT scan of normal lungs were selected manually and superimposed over lesions. The final results showed that our approach is superior to others for nodule segmentation and its dice coefficient and hausdorff distance were significantly improved.
机译：结节分割在肺癌的检测和诊断中起着至关重要的作用。尽管如此，放射科医师的手动细分可能是耗时和劳动密集型的。近年来，深度学习方法对医学图像分割表现良好。生成的对抗网络（GAN）通常用于图像生成。在本文中，提出了GaN进行图像分割，并提出了一种名为聚合-U-Net GaN（奥兰）的网络，其应用于胸部计算断层扫描（CT）图像中的自动段结节。通过将U-Net与Deep聚合组合来修改的发电机，了解肺结节的特征，并使分割接近地面真理。我们使用称为组织增强的方法，手动选择来自正常肺胸部CT扫描的正常区域的300个贴片，并在病变上叠加。最终结果表明，我们的方法优于结节分割，其骰子系数和Hausdorff距离显着提高。

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  • 来源
    《Sensing and imaging》 |2020年第1期|39.1-39.16|共16页
  • 作者

    Zaifeng Shi; Qixing Hu; Yuhan Yue; Zhongqi Wang; Omar Mohammed Saif AL‑Othmani; Huilong Li;

  • 作者单位

    School of Microelectronics Tianjin University No. 92 Weijin Road Tianjin 300072 People’s Republic of China Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin 300072 People’s Republic of China;

    School of Microelectronics Tianjin University No. 92 Weijin Road Tianjin 300072 People’s Republic of China;

    School of Microelectronics Tianjin University No. 92 Weijin Road Tianjin 300072 People’s Republic of China;

    School of Microelectronics Tianjin University No. 92 Weijin Road Tianjin 300072 People’s Republic of China;

    School of Microelectronics Tianjin University No. 92 Weijin Road Tianjin 300072 People’s Republic of China;

    School of Microelectronics Tianjin University No. 92 Weijin Road Tianjin 300072 People’s Republic of China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Computed tomography scans; Generative adversarial network; Nodule segmentation; Aggregation-U-net GAN; Tissue augmentation;

    机译：计算断层扫描扫描;生成对抗性网络;结节细分;聚集 - U-Net GaN;组织增强;

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