Realistic lesion generation is a useful tool for system evaluation and optimization. In this work, we investigate a data-driven approach for categorical lung lesion generation using public lung CT databases. We propose a generative adversarial network with a Wasserstein discrimination and gradient penalty to stabilize training. We further included conditional inputs such that the network can generate user-specified lesion categories. Novel to our network, we directly incorporated radiomic features in an intermediate supervision step to encourage similar textures between generated and real lesions. We evaluated the network using lung lesions from the Lung Image Database Consortium (LIDC) database. The lesions are divided into two categories: solid vs. non-solid. We performed quantitative evaluation of network performance base on four criteria: 1) overfitting in terms of structural and morphological similarity to the training data, 2) diversity of generated lesions in terms of similarity to other generated data, 3) similarity to real lesions in terms of distribution of example radiomics features, and 4) conditional consistency in terms of classification accuracy using a classifier trained on the training lesions. We imposed a quantitative threshold for similarity based on visual inspection. The percentage of non-solid and solid lesions that satisfy low overfitting and high diversity is 96.9% and 88.6% of non-solid and solid lesions respectively. The distribution of example radiomics features are similar in the generated and real lesions indicated by a low Kullback-Leibler divergence score. Classification accuracy for the generated lesions are comparable with that for the real lesions. The proposed network is a promising approach for data-driven generation of realistic lung lesions.
Keywords: deep learning; generative adversarial network; lesion generation; virtual clinical trial.