Synthetic CT Generation from MRI Using Improved DualGAN

Apr 01, 2019 MIDL 2019 Conference Abstract Submission readers: everyone Show Bibtex
  • Keywords: Machine Learning, Deep Learning, Radiology, Computed Tomography, Magnetic Resonance Imaging
  • Abstract: Synthetic CT image generation from MRI images is necessary to create radiotherapy plans without the need of co-registered MRI and CT scans. Our model based on GAN with cycle consistency permits unpaired image-to-image translation. Perceptual loss function term and coordinate convolutional layer were added to improve the quality of translated images. The proposed architecture was tested on paired MRI-CT dataset, where the synthetic CTs were compared to corresponding original CT images. The MAE between the synthetic CT images and the real CT scans is 63 HU computed inside of the true CTs body shape.
  • Code Of Conduct: I have read and accept the code of conduct.
  • Remove If Rejected: Remove submission from public view if paper is rejected.
0 Replies

Loading