TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation
TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation
复制标题
TumorGAN：用于脑肿瘤分割的多模态数据增强框架
DOI：
10.3390/s20154203
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发表时间：
2020-08-01
期刊：
影响因子：
3.9
通讯作者：
Zheng, Haiyong
中科院分区：
文献类型：
--
作者：
Li, Qingyun;Yu, Zhibin;Zheng, Haiyong
采集成对医学成像数据所涉及的人力需求很高，严重阻碍了深度学习方法在肿瘤分割等医学图像处理任务中的应用。当收集多模式图像对时，情况进一步恶化。然而，这个问题可以通过生成性对抗性网络的帮助来解决，这种网络可以用来生成逼真的图像。在这项工作中，我们提出了一个新的框架，称为TumorGAN，以生成基于非配对对抗性训练的图像分割对。为了提高生成图像的质量，我们引入了区域知觉损失来提高鉴别器的性能。我们还开发了区域L1丢失来限制成像的脑组织的颜色。最后，我们在公共脑瘤数据集Brats 2017上验证了TumorGAN的性能。实验结果表明，将该方法生成的合成数据对应用于分割网络训练，可以有效地提高肿瘤分割的性能。
The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.