4 papers
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks
Yuhua Chen, Dan Ruan, Jiayu Xiao +6
Segmentation of multiple organs-at-risk (OARs) is essential for radiation therapy treatment planning and other clinical applications. We developed an Automated deep Learning-based…
Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy
Jie Fu, Kamal Singhrao, Minsong Cao +7
Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models,…
Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks
Ningning Zhao, Nuo Tong, Dan Ruan +1
Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image…
Male pelvic synthetic CT generation from T1-weighted MRI using 2D and 3D convolutional neural networks
Jie Fu, Yingli Yang, Kamal Singhrao +3
To achieve magnetic resonance (MR)-only radiotherapy, a method needs to be employed to estimate a synthetic CT (sCT) for generating electron density maps and patient positioning re…