MRI to CT Translation with GANs

01/16/2019

∙

We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation. Furthermore we describe and implement three state of the art convolutional neural network (CNN) and generative adversarial network (GAN) models where we report statistics and visual results of two of them.

READ FULL TEXT

MRI to CT Translation with GANs

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization

End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images

SA-GAN: Structure-Aware Generative Adversarial Network for Shape-Preserving Synthetic CT Generation

Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks

Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network

PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer

MRI to CT Translation with GANs

Related Research

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization

End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images

SA-GAN: Structure-Aware Generative Adversarial Network for Shape-Preserving Synthetic CT Generation

Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks

Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network

PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer