Medical image synthesis with advanced deep learning methods - Zhang, Zeng (2019)
Synthetic images can be used to assist many applications, such as helping improve the performance of classifiers, generation of realistic images to augment the data, which are difficult in some cases for medical image annotation and acquisition. This report studies the synthesis of heart computed tomography coronary angiography (CTCA) images with coronary arteries using R-sGAN network [1]. With a generator and discriminator, the R-sGAN network can generate images with the same content as the query image and the similar style as the style image. Each 3D CT image in the training and style datasets are split into 2D slices. The generator creates the synthesized image from random noise, and the discriminator is trained to tell the input being synthetic or real. We have experiments on the CTCA images. Illustrated by the results on the center part of CTCA, we show that the synthetic images are similar to the style CT data, and the coronary content can also be embedded into the synthetic images. A future application is on tiny structure segmentation such as coronary artery. Because the structure is very small and yet long across 3D space, annotation becomes a challenge. The synthetic image might give us a new way to increase the annotated dataset.