A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation.

ABSTRACT: Lesion segmentation is critical for clinicians to accurately stage the disease and determine treatment strategy. Deep learning based automatic segmentation can improve both the segmentation efficiency and accuracy. However, training a robust deep learning segmentation model requires sufficient training examples with sufficient diversity in lesion location and lesion size. This study is to develop a deep learning framework for generation of synthetic lesions with various locations and sizes that can be included in the training dataset to enhance the lesion segmentation performance. The lesion synthesis network is a modified generative adversarial network (GAN). Specifically, we innovated a partial convolution strategy to construct a U-Net-like generator. The discriminator is designed using

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10113707 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature