Automatic deep learning segmentation models has been shown to improve both the segmentation efficiency and the accuracy. However, training a robust segmentation model requires considerably large labeled training samples, which may be impractical. This study aimed to develop a deep learning framework for generating synthetic lesions that can be used to enhance network training. The lesion synthesis network is a modified generative adversarial network (GAN). Specifically, we innovated a partial convolution strategy to construct an Unet-like generator. The discriminator is designed using Wasserstein GAN with gradient penalty and spectral normalization. A mask generation method based on principal component analysis was developed to model various lesion shapes. The generated masks are then converted into liver lesions through a lesion synthesis network. The lesion synthesis framework was evaluated for lesion textures, and the synthetic lesions were used to train a lesion segmentation network to further validate the effectiveness of this framework. All the networks are trained and tested on the public dataset from LITS. The synthetic lesions generated by the proposed approach have very similar histogram distributions compared to the real lesions for the two employed texture parameters, GLCM-energy and GLCM-correlation. The Kullback-Leibler divergence of GLCM-energy and GLCM-correlation were 0.01 and 0.10, respectively. Including the synthetic lesions in the tumor segmentation network improved the segmentation dice performance of U-Net significantly from 67.3% to 71.4% (p<0.05). Meanwhile, the volume precision and sensitivity improve from 74.6% to 76.0% (p=0.23) and 66.1% to 70.9% (p<0.01), respectively. The synthetic data significantly improves the segmentation performance.
翻译:已经展示了自动深度学习分解模型,以提高分解效率和准确性。然而,培训一个稳健的分解模型需要大量标记的培训样本,这可能不切实际。这项研究旨在开发一个用于生成合成损伤的深学习框架,可用于加强网络培训。损伤合成网络是一个改良的基因对抗网络(GAN)。具体地说,我们发明了一个部分变动战略,以构建一个类似Unet的生成器。歧视器是用瓦瑟斯泰因GAN设计的,配有梯度罚款和光谱正常化。根据主要部件分析开发了一个遮罩生成方法,以模拟各种腐蚀形状。随后,生成的面罩通过一个损伤合成网络网络转换成肝脏损伤。 腐蚀合成合成框架被评估用于腐蚀感应调调调调调调调调调调调调调调调网。所有网络都经过了来自LITSIT的公共数据集的培训和测试。拟议方法产生的合成变色素分布非常相似,与使用过的纯值为 < 9. 精确度4 内分解的精度参数、 GLCM-L 和GCM-CM- 等值变化为显著变化的数值。