  The label annotations for chest X-ray image rib segmentation are time
consuming and laborious, and the labeling quality heavily relies on medical
knowledge of annotators. To reduce the dependency on annotated data, existing
works often utilize generative adversarial network (GAN) to generate training
data. However, GAN-based methods overlook the nuanced information specific to
individual organs, which degrades the generation quality of chest X-ray image.
Hence, we propose a novel Semantics guided Disentangled GAN (SD-GAN), which can
generate the high-quality training data by fully utilizing the semantic
information of different organs, for chest X-ray image rib segmentation. In
particular, we use three ResNet50 branches to disentangle features of different
organs, then use a decoder to combine features and generate corresponding
images. To ensure that the generated images correspond to the input organ
labels in semantics tags, we employ a semantics guidance module to perform
semantic guidance on the generated images. To evaluate the efficacy of SD-GAN
in generating high-quality samples, we introduce modified TransUNet(MTUNet), a
specialized segmentation network designed for multi-scale contextual
information extracting and multi-branch decoding, effectively tackling the
challenge of organ overlap. We also propose a new chest X-ray image dataset
(CXRS). It includes 1250 samples from various medical institutions. Lungs,
clavicles, and 24 ribs are simultaneously annotated on each chest X-ray image.
The visualization and quantitative results demonstrate the efficacy of SD-GAN
in generating high-quality chest X-ray image-mask pairs. Using generated data,
our trained MTUNet overcomes the limitations of the data scale and outperforms
other segmentation networks.
