  The performance of medical image analysis systems is constrained by the
quantity of high-quality image annotations. Such systems require data to be
annotated by experts with years of training, especially when diagnostic
decisions are involved. Such datasets are thus hard to scale up. In this
context, it is hard for supervised learning systems to generalize to the cases
that are rare in the training set but would be present in real-world clinical
practices. We believe that the synthetic image samples generated by a system
trained on the real data can be useful for improving the supervised learning
tasks in the medical image analysis applications. Allowing the image synthesis
to be manipulable could help synthetic images provide complementary information
to the training data rather than simply duplicating the real-data manifold. In
this paper, we propose a framework for synthesizing 3D objects, such as
pulmonary nodules, in 3D medical images with manipulable properties. The
manipulation is enabled by decomposing of the object of interests into its
segmentation mask and a 1D vector containing the residual information. The
synthetic object is refined and blended into the image context with two
adversarial discriminators. We evaluate the proposed framework on lung nodules
in 3D chest CT images and show that the proposed framework could generate
realistic nodules with manipulable shapes, textures and locations, etc. By
sampling from both the synthetic nodules and the real nodules from 2800 3D CT
volumes during the classifier training, we show the synthetic patches could
improve the overall nodule detection performance by average 8.44% competition
performance metric (CPM) score.
