Advertisement
Published in:
05-08-2019 | Original Article
Synthesis of CT images from digital body phantoms using CycleGAN
Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 10/2019
Log inActivate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Purpose
The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size.
Methods
We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms. The image quality was assessed in terms of anatomical accuracy and realistic noise properties. We performed two studies exploring various network and training configurations as well as a task-based adaption of the corresponding loss function.
Results
The CycleGAN using the Res-Net architecture and three XCAT input slices achieved the best overall performance in the configuration study. In the task-based study, the anatomical accuracy of the generated synthetic CTs remained high (\(\mathrm{SSIM} = 0.64\) and \(\mathrm{FSIM} = 0.76\)). At the same time, the generated noise texture was close to real data with a noise power spectrum correlation coefficient of \(\mathrm{NCC} = 0.92\). Simultaneously, we observed an improvement in annotation accuracy of 65% when using the dedicated loss function. The feasibility of a combined training on both real and synthetic data was demonstrated in a blood vessel segmentation task (dice similarity coefficient \(\mathrm {DSC}=0.83\pm 0.05\)).
Conclusion
CT synthesis using CycleGAN is a feasible approach to generate realistic images from simulated XCAT phantoms. Synthetic CTs generated with a task-based loss function can be used in addition to real data to improve the performance of segmentation networks.