  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.
