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We present a new data augmentation method to address the data scarcity problem in deep learning based automatic segmentation of liver tumors. A non-rigid registration algorithm was used to generate new anatomical variations of liver tumors from a publicly available benchmark dataset. Additionally, a conditional image-to-image generative adversarial network (GAN) was trained to translate the generated segmentation masks into corresponding textured CT volumes. We used a state-of the-art segmentation model to investigate the benefit of our data augmentation method. Experiments with varying amounts of synthetic data were conducted and the results show that our novel augmentation method improves tumor segmentation performance by approximately 3 %, outperforming similar data augmentation techniques.

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Data Augmentation for Liver Tumor Segmentation using Structure, Texture, and Contrast

  • 会议论文

  • 发表日期：

    2025年

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  • Serouj Khajarian,
  • Oliver Amft,
  • Stefanie Remmele

摘要

We present a new data augmentation method to address the data scarcity problem in deep learning based automatic segmentation of liver tumors. A non-rigid registration algorithm was used to generate new anatomical variations of liver tumors from a publicly available benchmark dataset. Additionally, a conditional image-to-image generative adversarial network (GAN) was trained to translate the generated segmentation masks into corresponding textured CT volumes. We used a state-of the-art segmentation model to investigate the benefit of our data augmentation method. Experiments with varying amounts of synthetic data were conducted and the results show that our novel augmentation method improves tumor segmentation performance by approximately 3 %, outperforming similar data augmentation techniques.