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Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks

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Abstract

The segmentation of the lesion region in gastroscopic images is highly important for the detection and treatment of early gastric cancer. This paper proposes a novel approach for gastric lesion segmentation by using generative adversarial training. First, a segmentation network is designed to generate accurate segmentation masks for gastric lesions. The proposed segmentation network adds residual blocks to the encoding and decoding path of U-Net. The cascaded dilated convolution is also added at the bottleneck of U-Net. The residual connection promotes information propagation, while dilated convolution integrates multi-scale context information. Meanwhile, a discriminator is used to distinguish the generated and real segmentation masks. The proposed discriminator is a Markov discriminator (Patch-GAN), which discriminates each \(\mathrm{N}\times \mathrm{N}\) matrix in the image. In the process of network training, the adversary training mechanism is used to iteratively optimize the generator and the discriminator until they converge at the same time. The experimental results show that the dice, accuracy, and recall are 86.6%, 91.9%, and 87.3%, respectively. These metrics are significantly better than the existing models, which proves the effectiveness of this method and can meet the needs of clinical diagnosis and treatment.

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Acknowledgements

The data was provided by the digestive endoscopy center of General Hospital of People’s Liberation Army.

Funding

This work was supported by the National Key R&D Program of China (2017YFB0403801) and the Natural National Science Foundation of China (NSFC) (61835015).

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Author notes

  1. Yaru Sun and Yunqi Li contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Yaru Sun, Pengfei Wang & Dongzhi He

  2. Department of Gastroenterology, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China

    Yunqi Li

  3. Department of Gastroenterology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China

    Zhiqiang Wang

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  1. Yaru Sun
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  2. Yunqi Li
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Correspondence to Dongzhi He or Zhiqiang Wang.

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Sun, Y., Li, Y., Wang, P. et al. Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks. J Digit Imaging 35, 459–468 (2022). https://doi.org/10.1007/s10278-022-00591-1

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  • DOI: https://doi.org/10.1007/s10278-022-00591-1

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